Systems and methods for steering an agenda based on user collaboration

ABSTRACT

A collaborative prediction system for altering predictive outcomes of dynamic processes may include a processor configured to store initial information about a specific dynamic process having a plurality of potentially differing outcomes, assign to the specific dynamic process a first likelihood of occurrence of at least one of the potentially differing outcomes, and receive, from a first system user, notification data, the notification data being associated with the specific dynamic process. The processor may be configured to transmit, based on the notification data, at least some of the stored initial information about the specific dynamic process to the second system user and the third system user, receive from the second system user, first additional information responsive to the at least some of the stored initial information about the specific dynamic process and impacting the specific dynamic process, and generate, based on the first additional information and the initial information, a second likelihood different from the first likelihood. The processor may further be configured to transmit to the first system user, the second system user, and the third user an indication of the second likelihood, receive from the third system user, second additional information impacting the specific dynamic process, generate, based at least in part on the second additional information, a third likelihood different from the first likelihood and the second likelihood, and transmit to the first system user, the second system user, and the third system user an indication of the third likelihood.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 62/326,618, filed Apr. 22, 2016, which isincorporated herein by reference in its entirety.

BACKGROUND Technical Field

This disclosure generally relates to systems and methods for formulatingpredictions and conducting analyses relating to policymaking. Morespecifically, and without limitation, the present disclosure relates tosystems and methods for automatically predicting outcomes of andanalyzing documents related to legislative, regulatory, and judicialprocesses.

Background Information

Organizations often follow policy making processes to best strategizehow to promote their interests within a governmental unit. For example,an organization might have particular interest in the outcome of aparticular policymaking process that might affect the organization'sday-to-day operations. One approach used by organizations is to monitorand conduct qualitative and quantitative analysis on the impact variousoutcomes of the policymaking process may have on the organizations'interests. A second approach is to provide direct or indirect input topolicymakers through outreach, advocacy, and educational initiatives.However, understanding what a policymaking process will impact is verydifficult without specialized knowledge. One approach for providinginput is through the use of a lobbyist. However, traditional lobbyistsare often expensive and may specialize at one level of government, forexample, federal, state, local, etc. Furthermore, the sheer volume ofpolicymaking and quantity of information involved in each policymakingprocess across different levels and governmental entities can make itimpractical for an internal analyst or a lobbyist to monitor, track, andparticipate in potentially relevant policymaking initiatives. Manualanalysis of policymaking requires a significant investment of time andfinancial resources with the difficulty of consistently and objectivelycollating related information while simultaneously accounting formultiple possible outcomes across different affected areas of interest.

With the emergence of the Internet, some algorithms have been developedthat can track policymaking processes and predict policy outcomes.However, existing algorithms are typically limited to one level ofgovernment and/or one governmental unit. For example, govtrack.usprovides a database limited to federal policies in the U.S. Congress.Further, existing tools are often limited in that they do not provideany analysis or context for the vast quantities of materials stored indisparate databases.

Another drawback is the simplicity of many existing algorithms. Forexample, applying a principal components analysis (PCA), which is astatistical process that uses an orthogonal transformation to convert aset of observations (in this case, policymaking outcomes and possiblecauses) into a set of linearly uncorrelated variables, based onco-sponsorship and/or voting records does not account for more complexindicators of political position. Similarly, logistic regression is onlyas accurate at predicting outcomes as the predetermined factors andweights that are input into the regression. Such algorithms often failto dynamically adjust in light of news, comments, or changing positionsof certain policymakers, or other variables that affect policymaking.Accordingly, there is a need for improved aggregation techniques coupledwith analysis that is both multi-variate and dynamic.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods that incorporate machine-learning and data aggregationtechniques to provide policymaking analyses and predictions. Thepolicymaking analyses and predictions may encompass a wide range ofpolicymaking processes (e.g., legislative, regulatory, and judicialprocesses) and may provide more accurate and targeted predictions.

There are many possible applications for such capabilities. For example,organizations that cannot afford analysts, government relations experts,or traditional lobbyists and organizations that engage in in-houselobbying may benefit from the disclosed systems and methods. Inaddition, the disclosed systems and methods may eliminate redundancieswhen analyzing data across governmental levels and units.

According to an aspect of the present disclosure, a hybrid predictionsystem is disclosed for aggregating electronic data from at least oneInternet server and proprietary data, to identify and initially predictan outcome of a future event and to subsequently update the initialprediction. The system may comprise at least one processor configured toexecute instructions and a memory storing the instructions. Theinstructions may comprise instructions to access data scraped from theInternet. The data may be associated with at least one future event. Theinstructions may further comprise instructions to store the scraped dataand determine, from the scraped data, an initial prediction of theoutcome of the at least one future event. The instructions may furthercomprise instructions to generate, from the scraped data, an initiallikelihood indication associated with the initial prediction andtransmit the initial prediction and the initial likelihood indication toa device. The device may be associated with one or more users. Theinstructions may further comprise instructions to receive proprietaryinformation, store the proprietary information, and determine, using thescraped data and the proprietary information, a subsequent likelihoodindication. The subsequent likelihood indication may reflect a change inthe initial likelihood associated with the initial prediction. Theinstructions may further comprise instructions to transmit thesubsequent likelihood indication to the device.

According to another aspect of the present disclosure, a method isdisclosed for aggregating electronic data from at least one Internetserver and proprietary data to identify and initially predict an outcomeof a future event and to subsequently update the initial prediction. Themethod may comprise accessing data scraped from the Internet. The datamay be associated with at least one future event. The method may furthercomprise storing the scraped data and determining, from the scrapeddata, an initial prediction of the outcome of the at least one futureevent. The method may further comprise generating, from the scrapeddata, an initial likelihood indication associated with the initialprediction and transmitting the initial prediction and the initiallikelihood indication to a device. The device may be associated with oneor more users. The method may further comprise receiving proprietaryinformation, storing the proprietary information, and determining, usingthe scraped data and the proprietary information, a subsequentlikelihood indication. The subsequent likelihood indication may reflecta change in the initial likelihood associated with the initialprediction. The method may further comprise transmitting the subsequentlikelihood indication to the device.

According to another aspect of the present disclosure, a system isdisclosed for normalizing aggregated electronic data from at least oneInternet server. The system may comprise at least one processorconfigured to execute instructions and a memory storing theinstructions. The instructions may comprise instructions to access datascraped from the Internet. The scraped data may have been gathered froma plurality of local databases for a plurality of temporal localmilestones. Each local database may store localized terms forcharacterizing the temporal local milestones, such that at least a firstlocalized term for at least a first temporal milestone in a first localdatabase associated with a first locale differs from at least a secondlocalized term for a similar second temporal milestone in a secondlocalized database associated with a second locale. The instructions mayfurther comprise instructions to store the temporal local milestones ina central database and determine a mapping of each of the localizedterms for each temporal local milestone to a normalized term for eachmilestone. The instructions may further comprise instructions toidentify, in a displayed timeline associated with the first locale, eachassociated temporal local milestone using the normalized term for eachmilestone, even when the normalized term is not officially recognized inthe first locale. The instructions may further comprise instructions toidentify, in a displayed timeline associated with the second locale,each associated temporal local milestone using the normalized term foreach milestone, even when the normalized term is not officiallyrecognized in the second locale.

According to another aspect of the present disclosure, a hybridprediction system is disclosed for aggregating electronic data from atleast one Internet server and proprietary data, to identify andinitially predict an outcome of a future event and to subsequentlyupdate the initial prediction. The system may include at least oneprocessor configured to execute instructions; and a memory storing theinstructions to access data scraped from the Internet, the data beingassociated with at least one future event; store the scraped data;determine, from the scraped data, an initial prediction of the outcomeof the at least one future event; generate, from the scraped data, aninitial likelihood indication associated with the initial prediction;transmit the initial prediction and the initial likelihood indication toa device; automatically receive proprietary information obtained throughautomated scraping of at least one proprietary data source; store theproprietary information; determine, using the scraped data and theproprietary information, a subsequent likelihood indication reflecting achange in the initial likelihood associated with the initial prediction;and transmit the subsequent likelihood indication to the device.

According to another aspect of the present disclosure, a method isdisclosed for normalizing aggregated electronic data from at least oneInternet server. The method may comprise accessing data scraped from theInternet. The scraped data may have been gathered from a plurality oflocal databases for a plurality of temporal local milestones. Each localdatabase may store localized terms for characterizing the temporal localmilestones, such that at least a first localized term for at least afirst temporal milestone in a first local database associated with afirst locale differs from at least a second localized term for a similarsecond temporal milestone in a second localized database associated witha second locale. The method may further comprise storing the temporallocal milestones in a central database and determining a mapping of eachof the localized terms for each temporal local milestone to a normalizedterm for each milestone. The method may further comprise identifying, ina displayed timeline associated with the first locale, each associatedtemporal local milestone using the normalized term for each milestone,even when the normalized term is not officially recognized in the firstlocale. The method may further comprise identifying, in a displayedtimeline associated with the second locale, each associated temporallocal milestone using the normalized term for each milestone, even whenthe normalized term is not officially recognized in the second locale.

Another aspect of the present disclosure is directed to a collaborativeprediction system for altering predictive outcomes of dynamic processes.The system may include a processor configured to store initialinformation about a specific dynamic process having a plurality ofpotentially differing outcomes, assign to the specific dynamic process afirst likelihood of occurrence of at least one of the potentiallydiffering outcomes, and receive, from a first system user, notificationdata, the notification data being associated with the specific dynamicprocess. The processor may be configured to transmit, based on thenotification data, at least some of the stored initial information aboutthe specific dynamic process to the second system user and the thirdsystem user, receive from the second system user, first additionalinformation responsive to the at least some of the stored initialinformation about the specific dynamic process and impacting thespecific dynamic process, and generate, based on the first additionalinformation and the initial information, a second likelihood differentfrom the first likelihood. The processor may further be configured totransmit to the first system user, the second system user, and the thirduser an indication of the second likelihood, receive from the thirdsystem user, second additional information impacting the specificdynamic process, generate, based at least in part on the secondadditional information, a third likelihood different from the firstlikelihood and the second likelihood, and transmit to the first systemuser, the second system user, and the third system user an indication ofthe third likelihood.

Another aspect of the present disclosure is directed to a collaborativeprediction method for altering predictive outcomes of dynamic processes.The method may be performed by a processor, and may include storinginitial information about a specific dynamic process having a pluralityof potentially differing outcomes, assigning to the specific dynamicprocess a first likelihood of occurrence of at least one of thepotentially differing outcomes, and receiving, from a first system user,notification data, the notification data being associated with thespecific dynamic process. The method may include transmitting, based onthe notification data, at least some of the stored initial informationabout the specific dynamic process to the second system user and thethird system user, receiving from the second system user, firstadditional information responsive to the at least some of the storedinitial information about the specific dynamic process and impacting thespecific dynamic process, and generating, based on the first additionalinformation and the initial information, a second likelihood differentfrom the first likelihood. The method may further include transmittingto the first system user, the second system user, and the third user anindication of the second likelihood, receiving from the third systemuser, second additional information impacting the specific dynamicprocess, generating, based at least in part on the second additionalinformation, a third likelihood different from the first likelihood andthe second likelihood, and transmitting to the first system user, thesecond system user, and the third system user an indication of the thirdlikelihood.

Another aspect of the present disclosure is directed to data patternanalysis system for determining patterns in aggregated electronic datafrom at least one Internet server. The system may include at least oneprocessor configured to access data scraped from the Internet toidentify information that identifies individuals with a role in anoutcome of at least one prospective future event related to a predefinedsubject matter, the information including portions unrelated to thesubject matter of the at least one prospective future event, process theinformation to determine patterns in portions of the data unrelated tothe predefined subject matter, determine an influence factor for each ofthe identified individuals, based, at least in part, on the patterns inthe data unrelated to the predefined subject matter, and predict theoutcome of the future event based at least in part on the patterns inthe data unrelated to the predefined subject matter.

Another aspect of the present disclosure is directed to a data patternanalysis method for determining patterns in aggregated electronic datafrom at least one Internet server. The method may be performed by aprocessor, and may include accessing data scraped from the Internet toidentify information that identifies individuals with a role in anoutcome of at least one prospective future event related to a predefinedsubject matter, the information including portions unrelated to thesubject matter of the at least one prospective future event, processingthe information to determine patterns in portions of the data unrelatedto the predefined subject matter, determining an influence factor foreach of the identified individuals, based, at least in part, on thepatterns in the data unrelated to the predefined subject matter, andpredicting the outcome of the future event based at least in part on thepatterns in the data unrelated to the predefined subject matter.

Another aspect of the present disclosure is directed to anInternet-based agenda data analysis system. The system may include atleast one processor configured to maintain a list of user-selectableagenda issues, present to a user via a user interface, the list ofuser-selectable agenda issues, and receive via the user interface, basedon a selection from the list, agenda issues of interest to anorganization. The processor may be configured to access informationscraped from the Internet to determine, for a plurality of policymakers,individual policymaker data from which an alignment position of eachpolicymaker on each of the agenda issues is determinable, calculatealignment position data from the individual policymaker data, thealignment position data corresponding to relative positions of each ofthe plurality of policymakers on each of the plurality of selectedissues, and transform the alignment position data into a graphicaldisplay that presents the alignment positions of multiple policymakers.

Another aspect of the present disclosure is directed to anInternet-based agenda data analysis method. The method may be performedby a processor, and may include maintaining a list of user-selectableagenda issues, presenting to a user via a user interface, the list ofuser-selectable agenda issues, and receiving via the user interface,based on a selection from the list, agenda issues of interest to anorganization. The method may include accessing information scraped fromthe Internet to determine, for a plurality of policymakers, individualpolicymaker data from which an alignment position of each policymaker oneach of the agenda issues is determinable, calculating alignmentposition data from the individual policymaker data, the alignmentposition data corresponding to relative positions of each of theplurality of policymakers on each of the plurality of selected issues,and transforming the alignment position data into a graphical displaythat presents the alignment positions of multiple policymakers.

Another aspect of the present disclosure is directed to a predictionsystem with an integrated communications interface. The system mayinclude at least one processor configured to scrape the Internet toidentify a currently pending legislative bill and to identifyinformation about legislators slated to vote on the pending bill. Theprocessor may be further configured to parse the information todetermine a tendency position for each legislator. The tendency positionmay reflect a prediction of how each legislator is likely to vote on thepending bill. The processor may be further configured to transmit fordisplay to a system user a virtual whipboard that groups legislatorsinto a plurality of groups based on determined tendency positions. Theprocessor may be further configured to receive from the system user aselected one of the plurality of groups of legislators selected for acommunication interaction based on the determined tendency position ofthe group. The processor may be further configured to access alegislator database that includes legislative communication addresses oflegislative personnel scraped from the Internet and divided into aplurality of legislative function categories and receive from the systemuser a selection of at least one of the plurality of legislativefunction categories. The processor may be further configured to export,to a communication platform, the communication addresses of thelegislative personnel associated with both the selected group oflegislative function categories and the selected one of the plurality ofgroups of legislators.

Another aspect of the present disclosure is directed to a predictionmethod that may include scraping the Internet to identify a currentlypending legislative bill and to identify information about legislatorsslated to vote on the pending bill, and parsing the information todetermine a tendency position for each legislator. The tendency positionmay reflect a prediction of how each legislator is likely to vote on thepending bill. The method may further include transmitting for display toa system user a virtual whipboard that groups legislators into aplurality of groups based on determined tendency positions and receivingfrom the system user a selected one of the plurality of groups oflegislators selected for a communication interaction based on thedetermined tendency position of the group. The method may furtherinclude accessing a legislator database that includes legislativecommunication addresses of legislative personnel scraped from theInternet and divided into a plurality of legislative function categoriesand receiving from the system user a selection of at least one of theplurality of legislative function categories. The method may alsoinclude exporting, to a communication platform, the communicationaddresses of the legislative personnel associated with both the selectedgroup of legislative function categories and the selected one of theplurality of groups of legislators.

Another aspect of the present disclosure is directed to a system forpredicting and prescribing actions for impacting policymaking outcomes.The system includes at least one processor configured to access firstinformation scraped from the Internet to identify, for a particularpending policy, information about a plurality of policymakers slated tomake a determination on the pending policy. The processor may be furtherconfigured to parse the scraped first information to determine aninitial prediction relating to an outcome of the pending policy. Theinitial prediction may include a prediction for each of the plurality ofpolicymakers and a measure of the propensity of each of the plurality ofpolicymakers to decide in accordance with the initial prediction. Theprocessor may be further configured to display to a system user theinitial prediction including how each of the plurality of policymakersis likely to decide and the associated propensity to decide in thepredicted manner and to access second information to identify an actionlikely to change at least one of the initial prediction and thepropensity of at least one policymaker, to thereby generate a subsequentprediction corresponding to an increase in a likelihood of achieving thedesired outcome. The processor may be further configured to display tothe system user a recommendation to take the action in order to increasethe likelihood of achieving the desired outcome.

Another aspect of the present disclosure is directed to a method forpredicting and prescribing actions for impacting policymaking outcomes.The method may include accessing first information scraped from theInternet to identify, for a particular pending policy, information abouta plurality of policymakers slated to make a determination on thepending policy. The method may also include parsing the scraped firstinformation to determine an initial prediction relating to an outcome ofthe pending policy, the initial prediction including a prediction foreach of the plurality of policymakers and a measure of the propensity ofeach of the plurality of policymakers to decide in accordance with theinitial prediction. The method may further include displaying to asystem user the initial prediction including how each of the pluralityof policymakers is likely to decide and the associated propensity todecide in the predicted manner. The method may also include accessingsecond information to identify an action likely to change at least oneof the initial prediction and the propensity of at least onepolicymaker, to thereby generate a subsequent prediction correspondingto an increase in a likelihood of achieving the desired outcome. Themethod may also include displaying to the system user a recommendationto take the action in order to increase the likelihood of achieving thedesired outcome.

Another aspect of the present disclosure is directed to a data analysissystem for aggregating data from at least one internet server andproviding a predicted outcome based on the aggregated data. The systemmay include at least one processor configured to receive event data fromthe at least one internet server. The processor may be furtherconfigured to receive, from the at least one internet server, alignmentdata associated with the event data. The alignment data may includesupport or opposition data of at least one individual relating to anoutcome of the event. The processor may be further configured to receivefrom a system user, via an interactive interface of a browser, a desiredoutcome indicator corresponding to the event and to generate, based onthe event data and the alignment data, assumption data. The assumptiondata may be output to the interface. The processor may be furtherconfigured to calculate, based on the assumption data and the desiredoutcome data, a success probability of the desired outcome, the successprobability being output to the interface. The processor may also beconfigured to generate a suggested alteration to the assumption datathat impacts the success probability, the suggested alteration beinggenerated based on the event data, the alignment data, and the successprobability.

Another aspect of the present disclosure is directed to a text analyticssystem that may ascertain sentiment about multi-sectioned documents andmay associate the sentiment with particular sections. The system mayinclude at least one processor configured to scrape the Internet fortext data associated with comments expressed by a plurality ofindividuals about a common multi-sectioned document. The comments maynot be linked to a particular section. The at least one processor may befurther configured to analyze the text data in order to determine asentiment associated with each comment; apply an association analysisfilter to the text data in order to correlate at least a portion of eachcomment with one or more sections of the multi-sectioned document; andtransmit for display to the system user a visualization of the sentimentmapped to one or more sections of the multi-sectioned document.

Another aspect of the present disclosure is directed to a method thatmay ascertain sentiment about multi-sectioned documents and associatethe sentiment with particular sections. The method may include scrapingthe Internet for text data associated with comments expressed by aplurality of individuals about a common multi-sectioned document. Thecomments may not be not linked to a particular section. The method mayfurther include analyzing the text data in order to determine asentiment associated with each comment; applying an association analysisfilter to the text data in order to correlate at least a portion of eachcomment with one or more sections of the multi-sectioned document; andtransmitting for display to the system user a visualization of thesentiment mapped to one or more sections of the multi-sectioneddocument.

Another aspect of the present disclosure is directed to a text analyticssystem that may predict whether a policy will be adopted. The system mayinclude at least one processor configured to access information scrapedfrom the Internet to identify text data associated with commentsexpressed by a plurality of individuals about a proposed policy. The atleast one processor may be further configured to analyze the text datain order to determine a sentiment of each comment; apply an influencefilter to each comment to determine an influence metric associated witheach comment; weight each comment using the influence metric; determinebased on an aggregate of the weighted comments, an indicator associatedwith adoption of the policy; and transmit the indicator to a systemuser.

Another aspect of the present disclosure is directed to a method thatmay predict whether a policy will be adopted. The method may includeaccessing information scraped from the Internet to identify text dataassociated with comments expressed by a plurality of individuals about aproposed policy; analyzing the text data in order to determine asentiment of each comment; applying an influence filter to each commentto determine an influence metric associated with each comment; weighingeach comment using the influence metric; determining based on anaggregate of the weighted comments, an indicator associated withadoption of the policy; and transmitting the indicator to a system user.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processor and perform any of the methodsdescribed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a depiction of an example of a system consistent with one ormore disclosed embodiments of the present disclosure.

FIG. 2 is a depiction of an example of a server rack for use in thesystem of FIG. 1.

FIG. 3 is a depiction of an example of a device for use by the user(s)of the system of FIG. 1.

FIG. 4A is a depiction of another example of a device for use by theuser(s) of the system of FIG. 1.

FIG. 4B is a side-view of the device of FIG. 4A.

FIG. 5 is a depiction of an example of a system for predicting anoutcome of a future event.

FIG. 6 is a flowchart of an example of a method for predicting anoutcome of a future event.

FIG. 7 is a depiction of the system of FIG. 5 and a first and secondorganization.

FIG. 8 is a flowchart of an example of a method of incorporatinggeographic sub-areas into the method of FIG. 6.

FIG. 9 is a flowchart of an example of a method of incorporatinginteractive geographic sub-areas into the method of FIG. 6.

FIG. 10 is a flowchart of an example of a method of incorporatinginterconnected data between policymakers into the method of FIG. 6.

FIG. 11 is a depiction of example inputs into the systems of FIGS. 5 and7.

FIG. 12A is a screenshot of an example of a graphical user interface forreceiving input from a user.

FIG. 12B is a screenshot of an example of a graphical user interface forcausing a geographical display including sub-areas to include a term ofinterest.

FIG. 12C is a screenshot of an example of a graphical user interface ofa geographical display including sub-areas to include informationspecific to the sub-areas.

FIG. 12D is a screenshot of an example of a graphical user interface forenabling a user to hover over a geographical display includingsub-areas.

FIG. 12E is a screenshot of an example of a graphical user interface forenabling a user to click on a geographical display including sub-areas.

FIG. 13 is a flowchart of an example of a method for normalizingaggregated electronic data.

FIG. 14A is a screenshot of an example of a graphical user interface fordisplaying one or more normalized terms in a timeline.

FIG. 14B is a second screenshot of an example of a graphical userinterface for displaying one or more normalized terms in a timeline.

FIG. 15 is a diagrammatic illustration of an example of a memory storingmodules and data for altering predictive outcomes of dynamic processes.

FIG. 16A is a diagrammatic illustration of an example of a graphicaluser interface used for user collaboration and feedback.

FIG. 16B is a diagrammatic illustration of an example of a graphicaluser interface used for user collaboration and feedback.

FIG. 16C is a diagrammatic illustration of an example of a graphicaluser interface used for user collaboration.

FIG. 16D is a diagrammatic illustration of an example of a graphicaluser interface used for user collaboration.

FIG. 16E is a diagrammatic illustration of an example of a graphicaluser interface used for searching and selecting a legislative bill.

FIG. 16F is a diagrammatic illustration of an example of a graphicaluser interface used for displaying a dashboard indicating a likelihoodof one or more outcomes related to the selected legislative bill.

FIG. 16G is a diagrammatic illustration of an example of a graphicaluser interface used for displaying a dashboard indicating tallied votesand a likelihood that the bill will pass into law.

FIG. 16H is a diagrammatic illustration of an example of a graphicaluser interface displaying a virtual whipboard.

FIG. 16I is a diagrammatic illustration of an example of a graphicaluser interface used for illustrating a tagging capability.

FIG. 17 is a flow chart illustrating an example of a process performedby the exemplary system in FIG. 1, in accordance with the disclosedembodiments.

FIG. 18 is a diagrammatic illustration of an example of a memory storingmodules and data for performing internet-based data pattern analysis, inaccordance with the disclosed embodiments.

FIG. 19A is a diagrammatic illustration of an example of a graphicaluser interface used for viewing unrelated legislator or other publicofficial data.

FIG. 19B is a diagrammatic illustration of an example of a graphicaluser interface used for viewing additional unrelated legislator or otherpublic official data.

FIG. 19C is a diagrammatic illustration of an example of a graphicaluser interface used for viewing additional unrelated legislator or otherpublic official data.

FIG. 19D is a diagrammatic illustration of an example of a graphicaluser interface used for viewing additional unrelated legislator or otherpublic official data.

FIG. 19E is a diagrammatic illustration of an example of a graphicaluser interface used for viewing information related to influencing anoutcome.

FIG. 20 is a flow chart illustrating an example of a process performedby the system in FIG. 1.

FIG. 21 is a diagrammatic illustration of an example of a memory storingmodules and data for performing internet-based agenda data analysis.

FIG. 22A is diagrammatic illustration of an example of a graphical userinterface used for presenting a list of user-selectable agenda issuesfor performing internet-based agenda data analysis.

FIG. 22B is diagrammatic illustration of an example of a graphical userinterface used for presenting a dashboard of alignment of bills withusers.

FIG. 22C is diagrammatic illustration of an example of a graphical userinterface used for presenting a dashboard where sector weights may beadjusted to provide a weighted score for each legislator.

FIG. 22D is a diagrammatic illustration of an example of a graphicaluser interface used for presenting a graphical display that includesalignment coordinates displayed in graphical form.

FIG. 23A is a flow chart illustrating an example of a process performedby the system in FIG. 1.

FIG. 23B is a flow chart illustrating an additional example of a processperformed by the exemplary system in FIG. 1.

FIG. 24 illustrates an example of a memory containing software modules.

FIG. 25A illustrates an example of a virtual whipboard.

FIG. 25B illustrates an example of a communication that may be generatedvia use of the virtual whipboard of FIG. 25A.

FIG. 26 is a flow chart illustrating an example of a method for using avirtual whipboard in conjunction with a communication system.

FIG. 27 illustrates an example of a memory containing software modules.

FIG. 28 illustrates an example of a policymaking outcome predictiondisplay.

FIG. 29 is a flow chart illustrating an example of a method forproviding a predicted outcome based on aggregated data.

FIG. 30 is a flow chart illustrating an example of a method forpredicting and prescribing actions for impacting policymaking outcomes.

FIG. 31 illustrates an example of a memory associated with a textanalytics system.

FIG. 32 illustrates an example of text analytics consistent with one ormore disclosed embodiments of the present disclosure.

FIG. 33 illustrates an example of a multi-sectioned document withcorrelated comments.

FIG. 34 illustrates another example of a multi-sectioned document withcorrelated comments.

FIG. 35 illustrates a flow chart of an example of a method forascertaining sentiment about a multi-sectioned document associating thesentiment with particular sections.

FIG. 36 illustrates an example of a memory associated with a textanalytics system.

FIG. 37 illustrates an example of a text analytics system consistentwith one or more disclosed embodiments of the present disclosure.

FIG. 38 illustrates an example of a prediction with an indicator of anoutcome.

FIG. 39 illustrates s a flow chart of an example of a method forpredicting regulation adoption.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

The disclosed embodiments relate to systems and methods for predictingand analyzing one or more aspects of a policymaking process. Embodimentsof the present disclosure may be implemented using a general-purposecomputer. Alternatively, a special-purpose computer may be built usingsuitable logic elements.

Embodiments of the present disclosure may provide more accurateprediction and more global analysis of policymaking than traditionallobbying and/or algorithms.

Policymaking generally results in the production of large numbers ofdocuments. These documents may be produced among various governmentallevels. One governmental level may comprise, for example, internationalgovernmental bodies (e.g., the European Council, the United Nations, theWorld Trade Organization, etc.). A second governmental level maycomprise, for example, federal governmental bodies (e.g., the UnitedStates, China, the United Kingdom, etc.). A third governmental level maycomprise, for example, state governmental bodies (e.g., New York,British Columbia, etc.). A fourth governmental level may comprise, forexample, county governmental bodies (e.g., San Bernardino County, EssexCounty, Abhar County, etc.). A fifth governmental level may comprise,for example, local governmental bodies (e.g., Chicago, Hidaj, Cambridge,etc.). Other governmental levels between those listed are possible(e.g., Chinese prefectures may exist between the county level and thestate (or province) level). Accordingly, as is evident, a wide range ofgovernmental levels exist and may produce a vast array of policymakingdocuments.

Policymaking documents produced at a given level of government may beproduced across a plurality of jurisdictions. For example, at thefederal level, documents may be produced by the United States, Belgium,etc. Policymaking documents produced at a given level of a givenjurisdiction may be produced across a plurality of governmental units.For example, within the United States, the U.S. Congress may comprise agovernmental unit, the U.S. Court of Appeals for the Federal Circuit maycomprise another governmental unit, etc. In addition, some governmentalunits may comprise a plurality of subunits; for example, the U.S. Housemay comprise a subunit of the U.S. Congress, the Bureau of LaborStatistics may comprise a subunit of the U.S. Department of Labor. Somesubunits may further comprise a plurality of sub-subunits. As usedherein, a “governmental unit” may refer to a unit, subunit, sub-subunit,etc.

In general, governmental units may be grouped into three categories.Legislatures comprise governmental units that produce laws (orlegislation), e.g., the U.S. Congress, the UK Parliament, etc.Legislatures usually sit for preset periods of time called “sessions.”Any particular piece of legislation may be termed a “legislative bill.”Generally, a number of documents are produced that relate to bills(e.g., one or more committee reports, transcripts of one or more floorproceedings and/or debates, etc.). These documents may be “legislativedocuments” and a group of these documents that relate to a single billmay be termed a “legislative history.”

Commissions or regulatory agencies comprise governmental units thatproduce regulations and/or enforcement actions (e.g., the Federal TradeCommission, the Federal Institute for Drugs and Medical Devices, etc.).Regulatory rules comprise rules and/or guidelines that may have theforce of law and may implement one or more pieces of legislation. Acollection of documents related to a particular rule or guideline maycomprise a “regulatory history.” In addition, commissions/agencies mayfurther comprise one or more panels and/or administrative judges thatrule on enforcement actions (e.g., actions to enforce antitrust laws,actions to enforce privacy laws, etc.).

Courts comprise governmental units that resolve disputes between parties(e.g., the U.S. District Court for Wyoming, the Akita District Court,etc.). Resolution of these dispute, or “court cases,” usually result inone or more written or oral “court decisions.” Decisions may includeintermittent decisions (e.g., decisions on motions to exclude evidence,minute orders, motions to remittitur, etc.), as well as final decisionson the merits.

As used herein, the term “policymaker” may refer to any person within agovernmental unit involved with producing policymaking documents. Thus,the term “policymaker” may include persons having a vote on a particularpolicy (e.g., a member of a congress or parliament, a member of aregulatory commission or agency, a judge sitting on a panel, etc.). Arecord of all previous votes that a policymaker has cast may be termed a“voting history.”

The term “policymaker” may also include persons with power to takeunilateral action (e.g., an attorney general, a president or primeminister, etc.). Furthermore, the term “policymaker” may also includepersons who support other policymakers yet do not possess a vote or anyunilateral authority (e.g., staffers, assistants, consultants,lobbyists, etc.).

The volume of documents produced during policymaking may be overwhelmingto track and manage, not only by hand, but even through the use ofautomated approaches. By hand, mere aggregation of relevant governmentaldocuments, news, etc., may impose too heavy of a cost, both with respectto time and financial cost. Even if the aggregation is automated,however, manual analysis may still impose a heavy time and financialcost. Moreover, traditional algorithms may be limited to one level ofgovernment and/or one governmental unit, as well as fail to account fora sufficient amount of the aggregated information. Systems and methodsof the present disclosure may alleviate one or more of these technicalproblems.

For example, systems and methods of the present disclosure may aggregatedocuments produced during and/or related to policymaking. In someembodiments, the disclosed systems and methods may convert eachaggregated document to one or more forms appropriate for machineanalysis. For example, each document may be represented as anN-dimensional vector with coordinates determined by one or more words,phrases, sentence, paragraphs, pages, and/or combinations thereof. Insome embodiments, a document vector may represent a superposition offeatures associated with the document. As used herein, a “feature” mayrefer to a term of interest or other linguistic pattern derived from thetext of or a subset of the text of the document. In addition, the term“feature” may also refer to one or more pieces of metadata eitherappended to or derived from the document. Further, the term “feature”may also refer to one or more pieces of data derived from at least onelinguistic pattern, e.g., a part-of-speech identification, syntacticparsing, mood/sentiment analysis, tone analysis, or the like. The term“feature” is also not limited to a single document but may represent oneor more relationships between documents.

In some embodiments, a value may be computed for each feature and formthe basis for the subsequent superposition. For example, if a featurecomprises whether a given term appears in the document, the value maycomprise +1 if the term appears and may comprise 0 if the term does notappear. By way of further example, the value of the feature may beweighted according to its rate of occurrence in the document (e.g., thevalue is greater if the term occurs more often), weighted according toits rate of occurrence across a plurality of documents (e.g., the valueis greater if the term is more unique to the document), or the like.

In some embodiments, other techniques may be used in lieu of or inaddition to the vector analysis described above. For example, featuresmay be assigned a multi-dimensional vector rather than a real value.Word embedding using neural networks or performing dimensionalityreductions are two examples of additional techniques that may be used toconvert features into formats appropriate for machine analysis.

In some embodiments, the machine analysis may include machine modeling.As used herein, a “model” may refer to any function or operator thatrepresents an association between an input feature vector(s) and anoutput prediction and/or likelihood. Further, a model may take intoaccount one or more parameters. In some embodiments, a feature, asdiscussed above may constitute a parameter. In the context of a futureevent, a “prediction” may refer to any one of a finite set of outcomesof that event. For example, in an election having three candidates,there may be three possible outcomes: the first candidate is elected,the second candidate is elected, or the third candidate is elected. Byway of further example, if a policymaker is casting a vote, there may beat least two possible outcomes: the policymaker votes yes (e.g., “yea”),or the policymaker votes no (e.g., “nay”). Further, in the context of aprediction, a “likelihood” may refer to a probability that theprediction will be fulfilled.

As used herein, an “outcome” is not limited to the resolution of a vote.The term “outcome” may refer to any possible future event with respectto one or more policies. For example, with respect to legislation,outcomes may include whether or not a particular bill will beintroduced, to which committees it will be assigned, whether or not itwill be recommended out of committee, whether or not it will be put to afloor vote, what the final tally of the floor vote will be, and thelike. Similarly, with respect to regulatory rules, outcomes may includewhether or not a particular rule will be promulgated, which persons orcompanies will submit comments thereon, whether the rule will be amendedin response to one or more comments, and the like. With respect to courtcases, outcomes may include whether or not a motion will be granted,granted in part, or denied, whether or not a party will be sanctioned,how much in damages a judge or jury will award, and the like.

Furthermore, an “outcome” may include the date associated with thefuture event (e.g., on what date the vote is taken, on what date a rulewill be published in the Federal Register, on what date a hearingconcerning one or motions is held, etc.). The term “outcome” may furtherinclude one or more effects that a policy has on existing policies. Forexample, an “outcome” may include one or more statutes that will beamended by a pending bill, one or more regulations that will be amendedby a pending regulatory rule, one or more judicial precedents that willbe affected by a pending judicial decision, etc.

The term “outcome” may also refer to an impact that a policy has on oneor more geographic areas, one or more sectors of an economy (e.g.,manufacturing or retail), one or more industries within an economy(e.g., health care industry or services industry), or one or morecompanies (e.g., a non-profit, a public corporation, a private business,or a trade association). As used herein, an “impact” may refer to anassessment of the qualitative (e.g., favorable or unfavorable) orquantitative (e.g., 9/10 unfavorable, $1 billion additional costs)effects of a policy. Further, in some embodiments, an outcome maycontain a subset of outcomes, and a group of outcomes may includeintermediate outcomes.

In some embodiments, the model may also output one or more scoresreflecting the weight that one or more input features may have on theoutcome prediction and/or likelihood. In certain aspects, the scores maybe integrated with the model. In other aspects, the scores may becomputed based on the model (e.g., by raw tallying of outcomes based ona plurality of input features, by tallying of outcomes subject to athreshold, etc.).

As used herein, a “model” is not limited to a function or operator withone set of inputs and one set of outputs. A “model” may haveintermediate operations in which, for example, a first operationproduces one or more outputs that comprise one or more features that arethen operated on by a second operation that produces one or more outputstherefrom. For example, a first operation may accept a full set offeatures as input and output a subset of the features that meet athreshold for statistical significant; a second operation may thenaccept the subset of features as input and output additional derivedfeatures; a third operation may then accept the subset of featuresand/or the derived features as input and output one or more predictionsand/or likelihoods.

In some embodiments, the disclosed systems and methods may generate amodel, at least in part, based on one or more partitions of featurevectors from a collection of documents according to one or moresimilarity measures. The collection of documents may include, at leastin part, one or more documents comprising a training set. As usedherein, a “training set” may refer to documents created for the purposeof training a model or may refer to documents created during or relatedto policymaking that have been manually analyzed.

Thus, the disclosed systems and methods may generate a model usingmachine learning. Further, consistent with disclosed embodiments, amodel may comprise a logistic regression model, a support vector machinemodel, a Naïve Bayes model, a neural network model, a decision treemodel, a random forest model, any combination thereof, or the like.Further, a model may be modified and/or updated using machine learningsuch that the model is modified during subsequent uses of the model.

In some embodiments, a plurality of models may be developed and appliedto one or more documents. For example, a plurality of predictions and/orlikelihoods may be output by the plurality of models. In certainaspects, a subsequent model may accept the plurality of predictionsand/or likelihoods as input and output a single prediction and/orlikelihood representing a combination and/or normalization of the inputpredictions and/or likelihoods.

The aforementioned techniques for aggregation and modeling may be usedwith one or more of the systems discussed below and/or with one or moreof the methods discussed below.

FIG. 1 is a depiction of a system 100 consistent with the embodimentsdisclosed herein. As depicted in FIG. 1, system 100 may comprise anetwork 101, a plurality of sources, e.g., source 103 a, 103 b, and 103c, a central server 105, and user(s) 107. One skilled in the art couldvary the structure and/or components of system 100. For example, system100 may include additional servers—for example, central server 105 maycomprise multiple servers and/or one or more sources may be stored on aserver. By way of further example, one or more sources may bedistributed over a plurality or servers, and/or one or more sources maybe stored on the same server.

Network 101 may be any type of network that provides communication(s)and/or facilitates the exchange of information between two or morenodes/terminals. For example, network 101 may comprise the Internet, aLocal Area Network (LAN), or other suitable telecommunications network.In some embodiments, one or more nodes of system 100 may communicationwith one or more additional nodes via a dedicated communications medium.

Central server 105 may comprise a single server or a plurality ofservers. In some embodiments, the plurality of servers may be connectedto form one or more server racks, e.g., as depicted in FIG. 2. In someembodiments, central server 105 may store instructions to perform one ormore operations of the disclosed embodiments in one or more memorydevices. Central server 105 may further comprise one or more processors(e.g., CPUs, GPUs) for performing stored instructions. In someembodiments, central server 105 may send information to and/or receiveinformation from user(s) 107 through network 101.

In some embodiments, sources 103 a, 103 b, and 103 c may comprise one ormore databases. As used herein, a “database” may refer to a tangiblestorage device, e.g., a hard disk, used as a database, or to anintangible storage unit, e.g., an electronic database. For example, alocal database may store information related to particular locale. Alocale may comprise an area delineated by natural barriers (e.g., LongIsland), an area delineated by artificial barriers (e.g., Paris), or anarea delineated by a combination thereof (e.g., the United Kingdom).Thus, the website of any governmental body may comprise a localdatabase.

In other embodiments, sources 103 a, 103 b, and 103 c may comprise oneor more news databases (e.g., the website of The New York Times or theAssociated Press (AP) RSS feed. As used herein, the term “news” is notlimited to information from traditional media companies but may includeinformation from blogs (e.g., The Guardian's Blog), websites (e.g., asenator's campaign website and/or institutional website), or the like.

In still other embodiments, sources 103 a, 103 b, and 103 c may compriseother databases. For example, sources 103 a, 103 b, and 103 c maycomprise databases of addresses, phone numbers, and other contactinformation. By way of further example, sources 103 a, 103 b, and 103 cmay comprise databases of social media activity (e.g., Facebook orTwitter). By way of further example, sources 103 a, 103 b, and 103 c maycomprise online encyclopedias or wikis.

Further, system 100 may include a plurality of different sources, e.g.,source 103 a may comprise a local database, source 103 b may comprise anews database, and source 103 c may comprise one of the other databases.In some embodiments, one or more sources may be updated on a rollingbasis (e.g., an RSS feed may be updated whenever its creator updates thefeed's source) or on a periodic basis (e.g., the website of a townnewspaper may be updated once per day). In certain aspects, one or moresources may be operably connected together (e.g., sources 103 b and 103c) and/or one or more sources may be operably independent (like source103 a).

In some embodiments, central server 105 may receive information from oneor more of the plurality of sources, e.g., sources 103 a, 103 b, and 103c. For example, central server 105 may use one or more known dataaggregation techniques in order to retrieve information from sources 103a, 103 b, and 103 c.

In some embodiments, network 101 may comprise, at least in part, theInternet, and central server 105 may perform scraping to receiveinformation from the plurality of sources. As used herein, “scraping” or“scraping the Internet” may include any manner of data aggregation, bymachine or manual effort, including but not limited to crawling acrosswebsites, identifying links and changes to websites, data transferthrough API's, FTP's, GUI, direct database connections through, e.g.using SQL, parsing and extraction of website pages, or any othersuitable form of data acquisition. In certain aspects, central server105 may execute one or more applications configured to function as webscrapers. A web scraper may comprise a web crawler and an extractionbot. A web crawler may be configured to find, index, and/or fetch webpages and documents. An extraction bot may be configured to copy thecrawled data to central server 105 or may be configured to process thecrawled data and copy the processed data to central server 105. Forexample, the bot may parse, search, reformat, etc., the crawled databefore copying it.

Information scraped from the plurality of sources may comprise web pages(e.g., HTML documents) as well as other document types (e.g., pdf, txt,rtf, doc, docx, ppt, pptx, opt, png, tiff, png, jpeg, etc.). The webscraper may be configured to modify one or more types of scraped data(e.g., HTML) to one or more other types of scraped data (e.g., txt)before copying it to central server 105.

The web scraper may run continuously, near continuously, periodically atscheduled collection intervals (e.g., every hour, every two hours,etc.), or on-demand based on a request (e.g., user 107 may send arequest to central server 105 that initiates a scraping session). Insome embodiments, the web scraper may run at different intervals fordifferent sources. For example, the web scraper may run every hour forsource 103 a and run every two hours for source 103 b. This may allowthe web scraper to account for varying excess traffic limits and/or toaccount for varying bandwidth limits that may result in suboptimalperformance or crashes of a source.

In some embodiments, manual operators may supplement the processesperformed by the web scraper. For example, a manual operator may assistwith indexing one or more web pages that employ anti-crawlingtechnology. By way of further example, a manual operator may assist withparsing data that the extraction bot cannot interpret.

User(s) 107 may connect to network 101 by using one or more devices withan operable connection to network 101. For example, user(s) 107 mayconnect to network 101 using one or more devices of FIG. 3 or 4(described below). In some embodiments, user(s) 107 may send informationto and receive information from central server 105 though network 101.

In some embodiments, user 107 may send proprietary information tocentral server 105 via network 101. For example, proprietary informationmay comprise information privy to user 107 like the results of a privatemeeting between user 107 and one or more persons, or non-publicorganizational actions. By way of further example, proprietaryinformation may comprise information obtained by user 107 from asubscription news service or other service requiring payment in exchangefor information. By way of further example, proprietary information maycomprise information generated by the user, such as through their ownefforts or by a collective group of, e.g., an organization. Accordingly,proprietary information may be described as information determinedthrough “proprietary research.” Moreover, in some embodiments,proprietary information may be considered to be non-scraped, and inother embodiments, proprietary information may be scraped from aresource (e.g., from a server).

FIG. 2 is a depiction of a server rack 200 for use in system 100 ofFIG. 1. As depicted in FIG. 2, server rack 200 may comprise amanagement/control server 201, one or more compute servers, e.g.,servers 203 a and 203 b, one or more storage servers, e.g., servers 205a and 205 b, and spare server 207. The number and arrangement of theservers shown in FIG. 2 is an example, and one of skill in the art willrecognize any appropriate number and arrangement is consistent with thedisclosed embodiments.

In some embodiments, one or more servers of server rack 200 may compriseone or more memories. For example, as depicted in FIG. 2,management/control server 201 comprises memory 209 a, compute server 203a comprises memory 209 b, compute server 203 b comprises memory 209 c,storage server 205 a comprises memory 209 d, storage server 205 bcomprises memory 209 e, and spare server 207 comprises memory 209 f. Amemory may comprise a traditional RAM, e.g., SRAM or DRAM, or othersuitable computer data storage. The one or more memories may storeinstructions to perform one or more operations of the disclosedembodiments. In addition, the one or more memories may store informationscraped from the Internet.

In some embodiments, one or more servers of server rack 200 may furthercomprise one or more processors. For example, as depicted in FIG. 2,management/control server 201 comprises processor 211 a, compute server203 a comprises processor 211 b, compute server 203 b comprisesprocessor 211 c, storage server 205 a comprises processor 211 d, storageserver 205 b comprises processor 211 e, and spare server 207 comprisesprocessor 211 f A processor may comprise a traditional CPU, e.g., anIntel®, AMD®, or Sun® CPU, a traditional GPU, e.g., an NVIDIA® or ATI®GPU, or other suitable processing device. In some embodiments, the oneor more processors may be operably connected to the one or morememories. Further, in some embodiments, a particular server may includemore than one processor (e.g., two processors, three processors, etc.).

In some embodiments, one or more servers of server rack 200 may furthercomprise one or more non-volatile memories. For example, as depicted inFIG. 2, management/control server 201 comprises non-volatile memory 213a, compute server 203 a comprises non-volatile memory 213 b, computeserver 203 b comprises non-volatile memory 213 c, storage server 205 acomprises non-volatile memory 213 d, storage server 205 b comprisesnon-volatile memory 213 e, and spare server 207 comprises non-volatilememory 213 f. A non-volatile memory may comprise a traditional diskdrive, e.g., a hard disk drive or DVD drive, an NVRAM, e.g., flashmemory, or other suitable non-volatile computer data storage. The one ormore non-volatile memories may store instructions to perform one or moreoperations of the disclosed embodiments. In addition, the one or morenon-volatile memories may store information scraped from the Internet.

In some embodiments, one or more servers of server rack 200 may furthercomprise one or more network interfaces. For example, as depicted inFIG. 2, management/control server 201 comprises network interface 215 a,compute server 203 a comprises network interface 215 b, compute server203 b comprises network interface 215 c, storage server 205 a comprisesnetwork interface 215 d, storage server 205 b comprises networkinterface 215 e, and spare server 207 comprises network interface 215 f.A network interface may comprise, for example, an NIC configured to usea known data link layer standard, such as Ethernet, Wi-Fi, FibreChannel, or Token Ring. In some embodiments, the one or more networkinterfaces may permit the one or more servers to execute instructionsremotely. In addition, the one or more network interfaces may permit theone or more servers to access information from the plurality of sources.

Server rack 200 need not include all components depicted in FIG. 2.Additionally, server rack 200 may include additional components notdepicted in FIG. 2 (e.g., a backup server or a landing server).

FIG. 3 is a depiction of an example of a device 300 for use by user(s)107 of system 100 of FIG. 1. For example, device 300 may comprise adesktop or laptop computer. As depicted in FIG. 3, device 300 maycomprise a motherboard 301 having a processor 303, one or more memories(e.g., memories 305 a and 305 b, a non-volatile memory 307, and anetwork interface 309). As further depicted in FIG. 3, network interface309 may comprise a wireless interface (e.g., an NIC configured toutilize Wi-Fi, Bluetooth, 4G, etc.). In other embodiments, networkinterface 309 may comprise a wired interface (e.g., an NIC configured touse Ethernet, Token Ring, etc.). In some embodiments, network interface309 may permit device 300 to send information to and receive informationfrom a network.

In some embodiments, device 300 may further comprise one or more displaymodules (e.g., display 311). For example, display 311 may comprise anLCD screen, an LED screen, or any other screen capable of displayingtext and/or graphic content to the user. In some embodiments, display311 may comprise a touchscreen that uses any suitable sensing technology(e.g., resistive, capacitive, infrared, etc.). In such embodiments,display 311 may function as an input device in addition to an outputmodule.

In some embodiments, device 300 may further comprise one or more userinput devices (e.g., keyboard 313 and/or a mouse (not shown)). Asfurther depicted in FIG. 3, the one or more display modules and one ormore user input devices may be operably connected to motherboard 301using hardware ports (e.g., ports 315 a and 315 b). For example, ahardware port may comprise a PS/2 port, a DVI port, an eSata port, a VGIport, an HDMI port, a USB port, or the like.

Device 300 need not include all components depicted in FIG. 3.Additionally, device 300 may include additional components not depictedin FIG. 3 (e.g., external disc drives, graphics cards, etc.).

FIG. 4A is a depiction of an example of a device 400 for use by user(s)107 of system 100 of FIG. 1. For example, device 400 may comprise atablet (e.g., an iPad or Microsoft Surface), or a cell phone (e.g., aniPhone or an Android smartphone). As depicted in FIG. 4A, device 400 maycomprise screen 401. For example, screen 401 may comprise an LCDtouchscreen, an LED touchscreen, or any other screen capable ofreceiving input from the user and displaying text and/or graphic contentto the user.

FIG. 4B is a side view of example device 400 that depicts examplehardware included within device 400. As depicted in FIG. 4B, device 400may comprise a processor 403, one or more memories (e.g., memories 405 aand 405 b), a non-volatile memory 407, and a network interface 409. Asfurther depicted in FIG. 4, network interface 409 may comprise awireless interface, e.g., an NIC configured to use Wi-Fi, Bluetooth, 4G,or the like. In some embodiments, network interface 409 may permitdevice 400 to send information to and receive information from anetwork.

Device 400 need not include all components depicted in FIG. 4.Additionally, device 400 may include additional components not depictedin FIG. 4 (e.g., external hardware ports, graphics cards, etc.).

Predicting Future Event Outcomes Based on Data Analysis

In some embodiments, systems and methods consistent with the presentdisclosure may determine one or more predictions for one or more futureevents. A prediction may refer to a specific outcome for a future event.An outcome may, for example, be a resolution of a vote or other possiblefuture event with respect to one or more policies.

Systems and methods of the present disclosure may also determine alikelihood for the one or more predictions. A likelihood may refer to aprobability that the prediction will be fulfilled. For example, alikelihood may be a probability associated with a particular voteresolution or other possible outcome of a future event with respect toone or more policies.

FIG. 5 is a depiction of a memory 500 storing program modules and datafor predicting an outcome of a future event. In some embodiments, memory500 may be included in, for example, central server 105, discussedabove. Further, in other embodiments, the components of memory 500 maybe distributed over more than one location (e.g., stored in a pluralityof servers in communication with, for example, network 101).

As depicted in FIG. 5, memory 500 may include a policymaker database501. Policymaker database 501 may store information related to policiesand policymakers that is aggregated from a plurality of sources and/orparsed via machine analysis. For example, policymaker database 501 maystore information related to one or more future events and indexed bypolicy and/or policymaker.

As further depicted in FIG. 5, memory 500 may include a database accessmodule 503. Database access module 503 may control access to theinformation stored in database 501. For example, database access module503 may require one or more credentials from a user or anotherapplication in order to receive information from policymaker database501.

Memory 500 may further include a system user input module 505. Systemuser input module 505 may receive input from one or more users of memory500. For example, a user may send input to system user input module 505using one or more networks (e.g., network 100) operably connected to aserver (e.g., central server 105) storing system user input module 505.

As depicted in FIG. 5, memory 500 may further include an actionexecution module 507. Action execution module 507 may manage one or moreexecution lists executed on one or more processors (not shown) operablyconnected to memory 500. For example, action execution module 507 maypermit for multi-threading in order to increase the efficiency withwhich one or more execution lists are executed.

Memory 500 may also include an information identification module 509.Information identification module 509 may associate one or moreidentities with information received from policymaker database 501 usingsystem user input module 505. For example, information identificationmodule 509 may associate an identity of a policymaker with a news storyreceived from database 501 via system user input module 505. By way offurther example, information identification module 509 may associate anidentity of a policy with a legislative report received from policymakerdatabase 501 via system user input module 505.

As further depicted in FIG. 5, memory 500 may include a prediction andlikelihood identification module 511. Prediction and likelihoodidentification module 511 may generate and/or apply one or more modelsas discussed above. For example, prediction and likelihoodidentification module 511 may receive information from policymakerdatabase 501 using system user input module 505 and use the receivedinformation as input in one or more models. After applying one or moremodels, prediction and likelihood identification module 511 may outputone or more predictions and/or one or more likelihoods related to afuture event.

FIG. 6 is a flowchart of exemplary method 600 for predicting an outcomeof a future event, consistent with disclosed embodiments. Method 600may, for example, be executed by one or more processors of a server(e.g., central server 105 of FIG. 1) or any other appropriate hardwareand/or software. Further, when executing method 600, the one or moreprocessors may execute instructions stored in one or more of the modulesdiscussed above in connection with FIG. 5.

At step 610, the server may access scraped data. For example, the servermay implement one or more techniques for scraping data from the Internetas, described above. In other embodiments, the server may access datafrom a separate web scraper.

At step 620, the server may store the scraped data. For example, theserver may store the scraped data in a database (e.g., policymakerdatabase 501). In some embodiments, the server may parse the data priorto storing it and/or associated one or more identities with the dataprior to storing it. For example, the server may remove one or moreformatting tags from a scraped HTML documents before storing thedocument. By way of a further example, the server may associate ascraped document with one or more policymakers and/or one or morepolicies before storing it.

In some embodiments, the server may receive previously scraped andstored data from one or more databases in lieu of accessing scraped dataand storing it.

At step 630, the server may determine an initial prediction regarding afuture event based on the scraped data. For example, the server mayapply one or more models with some or all of the scraped data as one ormore inputs. Instead of using raw scraped data, the server may extractone or more features from the data, as discussed above, to use as inputsfor the model.

In some embodiments, the server may identify the future event, at leastin part, via a query from a user. For example, the server may receive aquery from a user via a data input terminal. The data input terminal maycomprise a device associated with the user, e.g., a cell phone, atablet, or other personal computing device. For example, the user mayinput the number of a pending legislative bill, and the server mayidentify the future event as the outcome of a floor vote on that bill.Similarly, the user may input the number of a pending regulatory rule,and the server may identify the future event as the enactment ornon-enactment of the pending rule. By way of further example, the usermay input the number of a pending court case, and the server mayidentify the future event as which party the jury will find in favor of.

In some embodiments, the server may receive the query before accessingscraped data or before storing the data. For example, the server maydetermine, at least in part, which scraped data to access based on thequery. The server may determine which scraped data to access based onpreexisting tags in the data and/or based on a dynamic determination ofrelevance. For example, if the user query included “healthcare,” theserver may determine which scraped data to access based on whether thedata was tagged as related to “healthcare.”

By way of a further example, if the future event involves a vote on alegislative bill, the server may access one or more websites ofgovernmental bodies to identify pending legislation related to the query(in this example, “healthcare”). Similarly, if the future event involvesadoption of a government regulation, the server may access one or morewebsites of governmental bodies to identify pending regulations relatedto the query (in this example, “healthcare”). By way of a furtherexample, if the future event involves a decision in a court case, theserver may access one or more websites of governmental bodies toidentify pending cases related to the query (in this example,“healthcare”).

At step 640, the server may determine an initial likelihood regardingthe initial prediction. For example, if the initial likelihood is that aparticular bill is likely to pass a legislature, the server may thendetermine that the bill has a 62% likelihood of passing. In someembodiments, the server may use the same model(s) that determined theinitial prediction to determine the initial likelihood. In otherembodiments, the server may use one or more different models, eitherseparately or in combination with the model(s) used to determine theinitial prediction, to determine the initial likelihood.

For example, the server may determine the initial likelihood based onone or more voting histories or records of one or more policymakers. Inthis example, the server may access the voting history of a policymaker(e.g., using policymaker database 501) and then determine the likelihoodof that policymaker voting a particular way on a particular policy basedon similarities between the policy and other policies included in thevoting history. In this example, the server may determine the initiallikelihood based on an aggregation of the likelihoods associated witheach policymaker.

By way of a further example, the server may determine the initiallikelihood based on one or more identified policymakers supporting andopposing a policy and an influence measure associated with each of theidentified policymakers. For example, the server may identify one ormore members of the National Assembly of South Korea that support and/oroppose a particular policy and then predict how other members will votebased on influence measures associated with the identified member(s).The server may determine the initial likelihood based on an aggregationof the predictions associated with each policymaker.

In some embodiments, the server may perform steps 630 and 640simultaneously. For example, the server may use one or more models thatgenerate an initial prediction in conjunction with an initiallikelihood.

At step 650, the server may transmit the initial prediction and initiallikelihood to one or more devices. For example, the server may transmitthe initial prediction and initial likelihood to a device associatedwith a user, e.g., a cell phone, a tablet, or other personal computingdevice.

At step 660, the server may receive proprietary information. Forexample, the server may receive the information from a user via a deviceassociated with the user or from a server over a network (e.g., theInternet). As discussed above, proprietary information may includeinformation privy to the user, such as the results of a private meetingbetween the user and one or more persons or information obtained by theuser from a subscription news service or other service requiring paymentin exchange for information. In some embodiments, the proprietaryinformation may constitute non-scraped proprietary information. In otherembodiments, the server may automatically receive proprietaryinformation obtained through automated scraping of at least oneproprietary data source.

In some embodiments, the server may receive the proprietary informationvia a data input terminal. For example, as discussed above, the datainput terminal may comprise a device associated with the user, e.g., acell phone, a tablet, or other personal computing device. For example,the user may enter into the data terminal that a specific policymakerwill vote a particular way based on a meeting between the user and thepolicymaker. In some embodiments, the server may receive proprietaryinformation by scraping a source provided by the user. For example, auser may provide access to proprietary source, e.g. an internal network,a database, an API.

At step 670, the server may store the received proprietary information.For example, the proprietary information may be stored in policymakerdatabase 501 or in a separate database. In some embodiments, the systemmay use the received proprietary information without storing it.

At step 680, the system may determine a subsequent likelihood based onthe scraped information and the proprietary information. For example, auser may input, as proprietary information, information indicating oneor more policymakers will vote differently than in the initialprediction. In this example, the system may then determine a subsequentlikelihood based on the scraped information that produced that initiallikelihood and the new vote(s) of one or more policymakers input by theuser.

In some embodiments, the user may provide access to a database ofproprietary information such as, for example, financial contributions toone or more policymakers. In this example, the server may then determinea subsequent likelihood based on the scraped information that producedthat initial likelihood and the financial contributions to one or morepolicymakers provided by the user.

By way of a further example, a user may input, as non-scrapedproprietary information, information indicating a pending bill will beamended to include new and/or different language. In this example, theserver may then determine a subsequent likelihood based on the scrapedinformation that produced that initial likelihood and the new and/ordifferent language input by the user.

In some embodiments, the server may use the same model(s) used todetermine the initial likelihood to determine the subsequent likelihood.In other embodiments, the server may use one or more different models,either separately or in combination with the model(s) used to determinethe initial likelihood, to determine the subsequent likelihood.

At step 690, the server may transmit the subsequent likelihood to one ormore devices. For example, the server may transmit the initialprediction and initial likelihood to a device associated with a user,e.g., a cell phone, a tablet, a smart watch, or other personal computingdevice. The server may transmit the subsequent likelihood to a deviceassociated with the user that inputted the proprietary informationand/or a user different from the user that inputted the proprietaryinformation.

Systems and methods consistent with the present disclosure may calculateinitial predictions, initial likelihoods, and subsequent likelihoods fora plurality of users. One or more subsets of the plurality of users mayprefer to share some or all predictions, likelihoods, proprietaryinformation, and the like. Other subsets of the plurality of users mayprefer to keep private some of all predictions, likelihoods, proprietaryinformation, and the like. Systems and methods consistent with thepresent disclosure may allow for subsets of users to select and enforcesuch desired privacy settings.

FIG. 7 is a depiction of a system 700 adapted to include a first andsecond organization. Exemplary system 700 may comprise a variation ofcentral server 105 of FIG. 1. As depicted in FIG. 7, system 700 mayinclude a prediction system 701. Prediction system 701 may includememory 500 of FIG. 5. As depicted in FIG. 5, module 705 and module 709may be located within a memory of system 700 or may be located onanother server. Prediction system 701 may be configured to execute, forexample, method 600 of FIG. 6.

As further depicted in FIG. 7, system 700 may include a firstorganization 703 operably connected to prediction system 701 via a firstorganization access module 705. First organization 703 may comprise oneor more users (e.g., user 107 of FIG. 1), operably connected to firstorganization access module 705 via one or more devices associated withthe user(s). Similarly, system 700 may include a second organization 707operably connected to prediction system 701 via a second organizationaccess module 709. Second organization 707 may also comprise one or moreusers (e.g., user 107 of FIG. 1), operably connected to secondorganization access module 709 via one or more devices associated withthe user(s).

As used herein, an “organization” may refer to a legally cognizableorganization such as a corporation, one or more official groups internalto a legally cognizable organization such as human resources, or one ormore unofficial groups internal to a legally cognizable organizationlike a project team or working group. The term “organization” may alsorefer to one or more groups of employees across different legallycognizable organizations or one or more groups of individual persons. Asused herein, the term “individual” includes any person, government,corporation, or organization. Accordingly, one or more users of system700 may self-organize themselves into a first organization or a secondorganization.

In some embodiments, first organization access module 705 may requireauthentication from a user that confirms the user is a member of thefirst organization in order to access prediction system 701. Similarly,in some embodiments, second organization access module 709 may requireauthentication from a user that confirms the user is a member of thesecond organization in order to access prediction system 701. Forexample, first organization access module 705 and second organizationaccess module 709 may receive a password, a fingerprint, or otheridentifier and compare the received identifier to a stored identifierassociated with the first or second organization. In some embodiments,first organization access module 705 and second organization accessmodule 709 may hash the received identifier before comparing the hashedidentifier to a stored identifier that is stored in a hashed format.

Further, first organization access module 705 may determine whetherusers associated with other organizations are permitted to accessproprietary information input by users associated with the firstorganization and/or subsequent likelihoods determined therefrom.Similarly, second organization access module 709 may determine whetherusers associated with other organizations are permitted to accessproprietary information input by users associated with the secondorganization and/or subsequent likelihoods determined therefrom. Forexample, if the first organization and the second organization agree tocollaborate, first organization access module 705 and secondorganization access module 709 may allow each organization to accessproprietary information input by the other organization. Moreover, firstorganization access module 705 and second organization access module 709may allow each organization to access subsequent likelihoods (which maybe termed “first organizational updates” and/or “second organizationalupdates”) determined using the proprietary information input by theother organization.

Accordingly, if predication system 701 or central server 105 or the likedetermines an initial prediction, an initial likelihood, and asubsequent likelihood based on proprietary information from one or moreusers associated with the first organization, system 701 may store theinitial prediction, initial likelihood, subsequent likelihood, andproprietary information in a manner preventing access by one or moreusers associated with a second organization. In such an example, thesecond organization may be referred to as an “unrelated organization.”By way of a further example, system 700 may store the initialprediction, initial likelihood, subsequent likelihood, and proprietaryinformation in a manner preventing access to some of the information(e.g., the subsequent likelihood, the proprietary information) andpermitting access to other of the information (e.g., the initialprediction, the initial likelihood) by one or more users associated witha second organization. In a third example, system 700 may store theinitial prediction, initial likelihood, subsequent likelihood, andproprietary information in a manner permitting access to the informationby one or more users associated with a second organization. Thus, system700 may allow for customization of sharing settings by a plurality oforganizations.

Addition collaborative setups using system 700 are possible. Forexample, collaborative agreements involving more than two organizationsare possible. By way of further example, collaborative agreementsinvolving a subset of proprietary information and/or a subset ofsubsequent likelihoods are also possible.

As discussed above, systems and methods consistent with the presentdisclosure may relate to policymaking among various governmental levels(e.g., an international level, a federal level, a state level, a countylevel, a local level, etc.). To more easily communication informationrelating to nested governmental levels, systems and methods consistentwith the present disclosure may allow for graphic displays of multiplesub-areas comprising one or more levels of government within a largerarea comprising one or more higher levels of government.

FIG. 8 is a flowchart of a method 800 of incorporating geographicsub-areas into method 600 of FIG. 6. Method 800 may, for example, beexecuted by one or more processors of a server (e.g., central server 105of FIG. 1) or any other appropriate hardware and/or software. Further,when executing method 800, the one or more processors may executeinstructions stored in one or more of the modules discussed above.

At step 810, the server may transmit a graphical display havingsub-areas to one or more devices. For example, the server may transmitthe graphical display to a device associated with a user, e.g., a cellphone, a tablet, or other personal computing device. In someembodiments, the graphical display may represent one or more regionscorresponding to a government level having a plurality of sublevels. Thesublevels may correspond to the sub-areas included in the graphicaldisplay.

At step 820, the server may scrape the Internet for data related to theplurality of sub-areas included in the graphical display. For example,if the graphical display comprises Canada, the server may scrape theInternet for data related to provinces of Canada. In some embodiments,the server may access stored information scraped from the Internetrather than actively scraping the Internet.

At step 830, the server may tag data included in the scraped data asrelated to one or more sub-areas. For example, the server may apply asearching algorithm to tag any data related to one or more sub-areas.The server may apply the searching algorithm to the data itself or tometadata associated with the data. By way of further example, the servermay apply other algorithms to determine if any data is related to one ormore sub-areas (e.g., by determining the source of the data).

At step 840, the server may receive a term of interest from a user. Forexample, the user may send the term of interest to a device associatedwith the user, which then transmits the term of interest to the server.In some embodiments, the server may execute step 840 before step 810,step 820, or step 830.

At step 850, the server may cause the graphical display to include theterm of interest. For example, the server may modify the graphicaldisplay to indicate whether the term of interest appears in policymakingdata corresponding to each sub-area. In such an example, the server maymodify the graphical display to quantify the extent to which the term ofinterest appears for each sub-area. For example, the server may apply acolor-shading scheme such that a sub-area having more policymaking dataincluding the term of interest is shaded more heavily than a sub-areahaving less policymaking data including the term of interest. Otheralgorithms quantifying the extent to which the term of interest appearsare possible (e.g., measuring the raw number of times the term ofinterest appears, etc.).

In some embodiments, step 810 and step 850 may be combined. For example,the server may wait to transmit the graphical display until after thegraphical display includes the term of interest.

At step 860, the server may enable interaction with the graphicaldisplay. For example, the server may enable a user receiving thegraphical display on a device associated with the user to interact withthe graphical display, e.g., by hovering over a sub-area or clicking asub-area. The interaction by the user may cause the device associatedwith the user to send an indicator signal to the server.

At step 870, the server may receive a selected sub-area. As explainedabove, a user may, for example, select a sub-area by hovering, clicking,or the like. The server may receive this selected sub-area, e.g., via adevice associated with the user. For example, the indicator signal mayindicate that the user has interacted with the graphical display andalso indicate which sub-area has been selected by the user via theinteraction.

At step 880, the server may transmit scraped data associated with theselected sub-area. For example, the user may click or hover over asub-area and thereafter receive scraped data associated with thatsub-area. In this example, the user may click or hover over a sub-areaand receive a list of pending bills or regulatory rules or court caseswithin the sub-area and related to the term of interest. The server maytransmit other scraped data associated with the selected sub-area (e.g.,the raw number of times the term of interest appears in pending bills,regulatory rules, or court cases within the selected sub-area, etc.).The graphical display may also include text, for example, indicating thenumber of pieces of legislation, number of regulatory rules, or numberof court cases associated with the selected sub-area.

By way of a further example, the server may transmit policymaking dataassociated with the selected sub-area and which contains the term ofinterest. In some embodiments, the policymaking data may be associatedwith multiple government bodies. For example, at least one of thegovernment bodies may comprise a legislature, and the policymaking datamay correspond to legislation. Similarly, at least one of the governmentbodies may comprise a commission, and the policymaking data maycorrespond to one or more regulations. By way of further example, atleast one of the government bodies may comprise a court, and thepolicymaking data may correspond to one or more court decisions.

In some embodiments, policymaking data may comprise at least one of abill number, a designation for the sub-area, a session, a title, adescription, a status, or at least one prediction relating to alikelihood of passage of the policymaking data. For example, the systemmay transmit one or more bill numbers, titles, descriptions, and/orstatuses associated with pending policies associated with the selectedsub-area. Similarly, the system may transmit a designation for thesub-area (e.g., a name) or a session (e.g., the 2016-2017 legislativesession, the November 2017 sitting, the 112th Congress, etc.) associatedwith the selected sub-area. By way of further example, the system maytransmit one or more predictions associated with the sub-area. Thetransmitted policymaking data may be displayed on the graphical displayor near the graphical display (e.g., in a list next to the graphicaldisplay).

Systems and methods consistent with the present disclosure may alsoallow for user interaction with the disclosed graphical displays. Forexample, a user may receive information related to one or more sub-areasvia interaction with the graphical display.

FIG. 9 is a flowchart of a method 900 of incorporating interactivegeographic sub-areas into method 600 of FIG. 6. Method 900 may, forexample, be executed by one or more processors of a server (e.g.,central server 105 of FIG. 1) or any other appropriate hardware and/orsoftware. Further, when executing method 900, the one or more processorsmay execute instructions stored in one or more of the modules discussedabove.

At step 910, the server may transmit a graphical display havingsub-areas to one or more devices. For example, the server may transmitthe graphical display to a device associated with a user (e.g., a cellphone, a tablet, or other personal computing device). In someembodiments, the graphical display may represent one or more regionscorresponding to a government level having a plurality of sublevels. Thesublevels may correspond to the sub-areas included in the graphicaldisplay.

In some embodiments, the graphical display may include tagged datarelated to one or more sub-areas. For example, the graphical display mayinclude scraped data tagged as relating to one or more sub-areas.

In some embodiments, the graphical display may include a term ofinterest received from a device associated with the user. For example,the graphical display may indicate whether the term of interest appearsin policymaking data corresponding to each sub-area. In such an example,the graphical display may include information that quantifies the extentto which the term of interest appears for each sub-area. For example,the graphical display may include a color-shading scheme such that asub-area having more policymaking data including the term of interest isshaded more heavily than a sub-area having less policymaking dataincluding the term of interest. Other schema quantifying the extent towhich the term of interest appears are possible (e.g., measuring the rawnumber of times the term of interest appears, etc.). The graphicaldisplay may also include text, for example, indicating the number ofpieces of legislation, number of regulatory rules, or number of courtcases associated with the selected sub-area.

At step 920, the server may enable interaction with the graphicaldisplay. For example, the server may enable a user receiving thegraphical display on a device associated with the user to interact withthe graphical display, e.g., by hovering over a sub-area or clicking asub-area.

At step 930, the server may transmit scraped data associated with aselected sub-area. For example, the user may click or hover over asub-area and thereafter receive scraped data associated with thatsub-area. In this example, the user may click or hover over a sub-areaand receive a list of pending bills or regulatory rules or court caseswithin the sub-area and related to the term of interest. The server maytransmit other scraped data associated with the selected sub-area, e.g.,the raw number of times the term of interest appears in pending bills,regulatory rules, or court cases within the selected sub-area, etc.

By way of further example, the server may transmit policymaking dataassociated with the selected sub-area, as described above with referenceto method 800.

Methods 800 and 900 may include additional steps. For example, methods800 and 900 may include receiving a request to filter a presentation toa specific legislative session. The request may be received from adevice associated with a user. In response to the request to filter, theserver may, after receiving the selection of the sub-area, display onlyscraped information relating to the specific legislative session.

FIG. 10 is a flowchart of a method 1000 of incorporating interconnecteddata between policymakers into method 600 of FIG. 6. Method 1000 may,for example, be executed by one or more processors of a server (e.g.,central server 105 of FIG. 1) or any other appropriate hardware and/orsoftware. Further, when executing method 1000, the one or moreprocessors may execute instructions stored in one or more of the modulesdiscussed above.

With respect to a single policymaker, systems consistent with thepresent disclosure may perform one or more analyses on the policymaker.For example, a disclosed system may calculate an ideology rating for thepolicymaker. The system may use a uni-dimensional or multi-dimensionalspace to map the ideological leanings of the policymaker. For example,with a uni-dimensional space, a policymaker may be scored as more“conservative” or more “liberal”; with a multi-dimensional space, apolicymaker may be scored as more “conservative” or more “liberal” onone issue (e.g., healthcare) and scored separately as more“conservative” or more “liberal” on other issues (e.g., immigration).

The ideological ends may vary as appropriate to the political culture inwhich the policymaker exists (e.g., “tory” versus “labour” rather than“conservative” versus “liberal”) and may include more than two ends asappropriate (e.g., “conservative,” “labour,” and “liberal democrat”). Anideology may also include other belief structures, such as “religious”versus “non-religious,” or a combination of a plurality of beliefstructures.

An ideological rating may be based on a plurality of factors, e.g., apolicymaker's voting history, a policymaker's history of cosponsorship,a policymaker's comments or statement on their website or a news storyor the like, financial contributions received by the policymaker, etc.As with modeling, a ranking algorithm may be trained using a trainingset coupled with machine learning.

In addition to ideology rankings, systems and methods consistent withthe present disclosure may generate an interconnectedness model. Forexample, in such a model, a plurality of policymakers may be representedas a network with each node representing a policymaker. In certainaspects, the edges of the network may be binary—that is, representing aconnection or lack thereof. In other aspects, the edges may be weighted,for example, with a higher weight indicating a closer relationshipbetween nodes. Weights may be calculated using a plurality of factors,such as the number of times two policymakers have voted together,sponsored together, received donations from similar organizations,attended the same school or schools, and the like.

In addition to ideology rankings, systems and methods consistent withthe present disclosure may generate an effectiveness score for one ormore policymakers. For example, an effectiveness score may represent howlikely a policy sponsored by that policymaker is likely to passcommittee, pass a vote, be enacted, or the like. An effectiveness scoremay be calculated based on overall activity, based on one or morelimited time periods, based on one or more particular policy areas(e.g., healthcare, tax, etc.), or the like.

Similar to effectiveness, systems consistent with the present disclosuremay generate a gravitas score for one or more policymakers. For example,a gravitas score may represent how likely that policymaker is to sway orinfluence other policymakers. A gravitas score may be calculated basedon the years the policymaker has served, the ranks the policymaker holds(e.g., in committees or organizations), or the like. A gravitas scoremay further be calculated based on an interconnectedness network, forexample, with a policymaker's gravitas score based on the number ofconnections and the closeness of those connections within the network.

At step 1010 of method 1000, the server may determine at least onepolicymaker. For example, the server may receive the determination froma user via a device associated with the user. In other embodiments, theserver may generate the determination using one or more algorithms. Forexample, the server may determine the at least one policymaker based onwhich policymakers occupy one or more leadership positions (e.g.,speaker, whip, chairperson, chief judge, etc.) within the policymakingbody. By way of further example, the server may determine the at leastone policymaker based on an effectiveness score, a gravitas score, orthe like.

At step 1020, the server may access data related to at least one otherpolicymaker. In some embodiments, the server may access the data viascraping or other aggregation techniques. In other embodiments, theserver may access the data from stored data in a database. The servermay also use a combination of aggregation and stored data to access datarelated to at least one other policymaker.

For example, the server may access and identify information about aplurality of policymakers slated to vote on a pending bill, or making adetermination on a pending rule, action, or case. In addition, theinformation may include voting records and party affiliation for thepolicymakers.

At step 1030, the server may identify interconnected matches (or“interconnected data matches”) between the at least one policymaker andthe at least one other policymaker. For example, as described above, theserver may generate an interconnectedness network using the at least onepolicymaker and the at least one other policymaker.

By way of further example, the server may determine trends in similarvoting patterns between the at least one policymaker and the at leastone other policymaker. In this example, the system may determine howoften the at least one policymaker and the at least one otherpolicymaker voted together on the same policy; based on this frequency,the system may predict whether the at least one policymaker and the atleast one other policymaker tend to vote together or not. Thisprediction may comprise a global prediction or may be limited to one ormore types of policy (e.g., bill, joint resolution, and the like) to oneor more areas of policy (e.g., taxes, healthcare, and the like), or toother categorizations.

By way of a further example, the server may determine influenceindicators that connect policymakers. For example, the server maydetermine an influence score that quantifies one policymaker's influenceover at least one other policymaker. As an example, the server maydetermine a high influence score for a Chief Justice and thus predictthat he or she is likely to influence certain judges to vote inparticular ways.

At step 1040, the server may determine a likelihood based on theinterconnected matches. For example, the server may determine thelikelihood of an outcome based on predicted positions of one or morepolicymakers as adjusted to account for the interconnectedness network.

By way of further example, the server may determine a likelihood basedon predicting how each of a plurality of policymakers is likely to voteor make the determination. For example, the server may determine howeach policymaker is likely to vote or make the determination based oninterconnected match data, e.g., trends in similar voting patterns,influence indicators, and the like. As an example, the system maydetermine a likelihood of a bill in the UK Parliament being enacted bydetermining that Prime Minister David Cameron has a strong influence onmost members of the Conservative Party and that Leader of the OppositionEd Miliband has a mild influence on most members of the Labour Partyand, based on these determinations, generating likelihoods on how eachMP within the Parliament is likely to vote or make the determination.

At step 1050, the server may transmit the determined likelihood to auser. For example, the server may transmit the likelihood to a deviceassociated with a user, e.g., a cell phone, a tablet, or other personalcomputing device.

FIG. 11 is a depiction of possible inputs into a system 1100. System1100 may comprise, for example, a variation of central server 105 ofFIG. 1. Moreover, system 1100 may include memory 500 of FIG. 5. Asdepicted in in FIG. 11, system 1100 may include prediction system 1105.In some embodiments, prediction system 1105 may comprise a variation ofcentral server 105 of FIG. 1, exemplary system 700 of FIG. 7, or thelike. Prediction system 1105 may further include memory 500 of FIG. 5.

As further depicted in FIG. 11, scraped data 1101 and proprietary data1103 may be input into prediction system 1105. Scraped data 1101 maycomprise data related to one or more policymakers, data related to oneor more policies, data related to one or more published news stories,data scraped from one or more social networks, or the like. Proprietarydata 1103 may comprise information related to one or more privatemeetings, data related to one or more subscription news stories, or thelike.

As depicted in FIG. 11, prediction system 1105 may generate a prediction1107 and a likelihood 1109 from scraped data 1101 and proprietary data1103. For example, system 1105 may apply one or more models, asdiscussed above, using scraped data 1101 and proprietary data 1103 asinputs. In some embodiments, system 1105 may parse and/or extract one ormore features from some or all of scraped data 1101 and/or proprietarydata 1103 before applying the data and/or features as inputs. Moreover,as discussed above, in some embodiments, proprietary data 1103 mayinclude scarped data and/or may include non-scraped data.

In addition to the hardware and software discussed above, systems andmethods of the present disclosure may be implemented, at least in part,using one or more graphical user interfaces (GUIs). For example, a usermay submit queries and/or non-scraped proprietary information via one ormore GUIs. By way of further example, interaction with one or moregraphical displays may be facilitated via one or more GUIs. Accordingly,steps of methods 600 of FIG. 6, 800 of FIG. 8, or 900 of FIG. 9 may befacilitated via the use of one or more GUIs.

FIG. 12A is a screenshot of a GUI 1200 for receiving input from a user.As depicted in FIG. 12A, GUI 1200 may include a text box 1201 configuredto allow a user to type input. As further depicted in FIG. 12A, GUI 1200may include a button 1203 configured to transmit the input typed intotext box 1201 to a remote system (not shown). For example, the remotesystem may comprise system 500 of FIG. 5, system 700 of FIG. 7, or thelike.

FIG. 12B is a screenshot of a GUI 1210 for causing a geographicaldisplay including sub-areas to include a term of interest. As depictedin FIG. 12B, GUI 1210 may include a term of interest 1205 that is mappedonto a geographical display 1207 including sub-areas, e.g., sub-areas1209 a and 1209 b. In the example of FIG. 12B, the number of legislativebills related to the term of interest is mapped onto each relevantsub-area. Other variables related to the term of interest (e.g., thenumber of pending regulatory rules related to the term of interest, thenumber of pending court cases related to the term of interest, thenumber of pending policies related to the term of interest with alikelihood of enactment above a threshold, etc.) may be mapped onto eachrelevant sub-area.

FIG. 12C is a screenshot of a GUI 1220 of a geographical displayincluding sub-areas to include information specific to the sub-areas. Asdepicted in FIG. 12C, GUI 1220 may include a geographical display 1211having sub-areas, e.g., sub-areas 1213 a and 1213 b, with one or morepieces of information specific to the sub-areas depicted in display1211. In the example of FIG. 12C, a score representing the agreementbetween a government of the sub-area and one or more opinions input bythe user is mapped onto each relevant sub-area. Other variables relatedto one or more opinions input by the user (e.g., the number ofpolicymakers having an agreement score above a threshold, etc.) may bemapped onto each relevant sub-area.

FIG. 12D is a screenshot of a GUI 1230 for enabling a user to hover overa geographical display including sub-areas. As depicted in FIG. 12D, GUI1230 may include a geographical display 1215 having sub-areas, e.g.,sub-area 1217. In the example of FIG. 12D, a user has hovered oversub-area 1217. In response to the hovering, GUI 1230 displaysinformation 1219 near sub-area 1217. In the example of FIG. 12D,information 1219 comprises the number of legislative bills withinsub-area 1217 related to the term of interest. Information 1219 maycomprise any other variable related to sub-area 1217 and the term ofinterest (e.g., the number of pending regulatory rules within sub-area1217 related to the term of interest, the number of pending court caseswithin sub-area 1217 related to the term of interest, the number ofpending policies within sub-area 1217 related to the term of interestwith a likelihood of enactment above a threshold, etc.).

FIG. 12E is a screenshot of a GUI 1240 for enabling a user to click on ageographical display including sub-areas. As depicted in FIG. 12E, GUI1240 may include a geographical display 1221 having sub-areas, e.g.,sub-area 1223. In the example of FIG. 12E, a user has clicked onsub-area 1223. In response to the clicking, GUI 1240 displays results1225 related to sub-area 1223. In the example of FIG. 12E, results 1225comprise a list of legislative bills within sub-area 1223 related to theterm of interest. Results 1225 may comprise any other list of itemsrelated to sub-area 1223 and the term of interest (e.g., a list ofpending regulatory rules within sub-area 1223 related to the term ofinterest, a list of pending court cases within sub-area 1223 related tothe term of interest, a list of pending policies within sub-area 1223related to the term of interest with a likelihood of enactment above athreshold, etc.).

Mapping to Milestones in a Policymaking Process

In parsing aggregated documents related to policymaking, systems andmethods consistent with the present disclosure may normalize one or moreterms of the documents. In some embodiments, the normalization may beapplied to one or more temporal milestones related to policymaking. Asused herein, a “temporal milestone” may refer to one or more steps in asequence of steps through which a policy may progress prior toenactment. For example, with respect to legislation, temporal milestonesmay include introduction of a bill, a bill passing a committee, a floorvote taken on the bill, and the like. By way of a further example, withrespect to regulatory rules, temporal milestones may includeintroduction of rule, opening of a notice-and-comment period, publishingof a revised rule, and the like. By way of a further example, withrespect to court cases, temporal milestones may include motion hearings,decisions on motions, opening statements, closing statements, and thelike.

Normalization may accept one or more local temporal milestones (or“local terms”) and replace them with global temporal milestones (or“normalized terms”). As used herein, the term “local temporalmilestones” refers to temporal milestones as used in one or morelocalities—that is, in one or more jurisdictions (e.g., a government).In some embodiments, the system may derive global milestones from one ormore of the local milestones. In other embodiments, the system mayreceive a new set of global milestones. Some jurisdictions may not havetemporal local milestones that correspond to every global temporalmilestone, and some jurisdictions may have temporal local milestonesthat do not have a corresponding global temporal milestone.

The replacing of local terms with normalized terms may include parsingone or more documents to identify local terms and mapping the identifiedlocal terms to normalized terms. Parsing may include using one or moremachine-learned algorithms or other computer-implemented rules, e.g.,term matching. Parsing may also include outputting one or more potentialidentifications of local terms and may include one or more confidencescores associated with the potential identifications.

Mapping may include substituting one or more local terms with normalizedterms. The mapping may include one-to-one associations, many-to-manyassociations, one-to-many associations, many-to-one associations, and/orcombinations thereof. A mapping may be modified and/or updated usingmachine learning or other computer-implemented rules such that themapping is modified during subsequent uses. As with parsing, mapping mayinclude outputting one or more potential normalized terms and mayinclude one or more confidence scores associated with the potentialnormalized terms. For example, mapping may accept local terms like“enacted,” “passed,” “succeeded,” “enforceable,” and the like, andoutput a normalized term like “enacted.” Further, in some embodiments,mapping may involve accessing a data structure such, as for example, alookup table.

Normalization may further be applied to citations within documents. Forexample, a local citation used in one or more localities may conform toone or more standardized citation formats (e.g., MLA, CPA, Bluebook) ormay conform to a standardized style or tradition established within thelocality. Normalization may replace one or more local citations withglobal citations.

Similarly, normalization may be applied to relational terms betweendocuments. For example, a local relational term used in one or morelocalities may represent relationships between one or more policymakingdocuments. For instance, the U.S. federal government publishes the“Federal Register” that contains “Authority Citations” to statutes thatare claimed to provide authority for the promulgation of one or moreregulatory rules; meanwhile, the Utah state government publishes the“Utah State Bulletin” that contains “Statutory Authorization” citationsto statutes that are claimed to provide authority for the promulgationof one or more regulatory rules. Normalization may replace one or morelocal relational terms (e.g., “Statutory Authorization”) with globalrelational terms (e.g., “Authority Citations”).

Normalization may further be applied to actor-based terms withindocuments. For example, a local relational term used in one or morelocalities may represent a relationship between a document and an actor.For instance, legislative bills in the U.S. Congress have “sponsors” and“cosponsors”; meanwhile, legislative bills in the Virginia GeneralAssembly have “patrons” and “copatrons.” Normalization may replace oneor more local actor-based terms (e.g., “patron”) with global actor-basedterms (e.g., “sponsor”).

Normalization may also include storing the local term and/or normalizedterm in one or more databases. For example, the terms may be stored inone or more databases or one or more lookup tables for use in futurenormalizations.

FIG. 13 is a flowchart of a method 1300 for normalizing aggregatedelectronic data. Exemplary method 1000 may, for example, be executed byone or more processors of a server (e.g., central server 105 of FIG. 1)or any other appropriate hardware and/or software. Further, whenexecuting method 1300, the one or more processors may executeinstructions stored in any one of the modules discussed above.

At step 1310, the server may access scraped data, for example, datascraped from the Internet. In some embodiments, the server may obtainthe data using one or more scraping or other aggregation techniques, asdiscussed above. In certain aspects, the server may also store theaccessed scraped data, for example, in policymaker database 501.

In other embodiments, the server may access the data stored in adatabase. For example, the server may access policymaker database 501 toaccess the scraped data. The server may also use a combination ofobtaining and accessing. By way of further example, accessing the datamay include parsing fields of documents associated with differinggovernment bodies. For example, the documents may be legislative(related to a legislature), regulatory (related to a commission), orjudicial (related to one or more courts).

In some embodiments, the scraped data may have been gathered from aplurality of local databases for a plurality of temporal localmilestones. For example, each local database may store localized termsfor characterizing the temporal local milestones. By way of a furtherexample, the server may access scraped data related to at least twolocales such that at least a first localized term for at least a firsttemporal milestone in a first local database associated with a firstlocale differs from at least a second localized term for a similarsecond temporal milestone in a second localized database associated witha second locale. For example, the first locale and the second local mayboth comprise state governments. By way of a further example, the firstlocale may comprise a federal government, and the second locale maycomprise a state government.

At step 1320, the server may identify and store one or more localtemporal milestones in the data. For example, the server may use one ormore machine-learned algorithms or other computer-implemented rules,e.g., term matching, to identify the local temporal milestones. By wayof a further example, the server may identify the term “sent tocommittee” as a local temporal milestone using a predefined list oflocal temporal milestones and/or machine learning algorithms.

In some embodiments, the server may store the identified milestone witha corresponding confidence score representing the robustness of theidentification. For example, the server may identify the term “sent tocommittee” as a local temporal milestone with 92% confidence. Theconfidence score could further be represented using decimals, integers,or any other appropriate scale.

In some embodiments, the local temporal milestones (also termed“localized terms”) may refer to events in a legislative history, eventsin a regulatory history, or events in a court case.

At step 1330, the server may determine a mapping. For example, themapping may include may include one-to-one associations between localmilestones and global milestones, many-to-many associations, one-to-manyassociations, many-to-one associations, and combinations thereof. Insome embodiments, the mapping may be created/modified and/or updatedusing machine learning such that the mapping is modified duringsubsequent uses. In some embodiments, the mapping may be stored inpolicymaker database 501. In other embodiments, the mapping may bestored in a separate database or lookup table.

At step 1340, the server may identify one or more global temporalmilestones corresponding to the local milestones using the mapping. Asin step 1320, the server may also generate a corresponding confidencescore representing the robustness of the identification. For example,the server may identified “assigned to committee” as a global temporalmilestone corresponding to the identified local temporal milestone of“sent to committee.” By way of a further example, the server may alsooutput the match with a confidence score of 84%. The confidence scorecould further be represented using decimals, integers, or any otherappropriate scale.

In some embodiments, identifying one or more global temporal milestonesmay include displaying a timeline associated with the locale containingeach associated temporal local milestone using the normalized term foreach milestone, even when the normalized term is not officiallyrecognized in the locale. In embodiments including at least two locales,the displayed timeline may be associated with the first or second localeand contain each associated temporal local milestone using thenormalized term for each milestone, even when the normalized term in notofficially recognized in the first or second locale. Moreover, inembodiments including at least two locales, the displayed timelineassociated with the first locale and the displayed timeline associatedwith second locale may be normalized in a common format despitedifferences between the locals. For example, a timeline for alegislative bill in Utah may be in the same format as a timeline for alegislative bill in the U.S. Congress.

FIG. 14A is a screenshot of a GUI 1400 for displaying one or morenormalized terms in a timeline. As depicted in FIG. 14A, GUI 1400 mayinclude a timeline 1401. As further depicted in FIG. 14A, timeline 1401may include one or more normalized terms, e.g., normalized term 1403,and one or more local terms, e.g., local term 1405.

FIG. 14B is a second screenshot of a GUI 1410 for displaying one or morenormalized terms in a timeline. Similarly to FIG. 14A, as depicted inFIG. 14B, GUI 1410 may include a timeline 1407. As further depicted inFIG. 14B, timeline 1407 may include one or more normalized terms, e.g.,normalized term 1409, and one or more local terms, e.g., local term1411.

Steering an Agenda Based on User Collaboration

In some embodiments, the disclosed systems and methods may involvecollaborative prediction for altering predictive outcomes of dynamicprocesses. For example, users may collaborate across a network in orderto steer legislative agendas or proposed legislation. Collaboration mayenable a legislative agenda to be advanced by permitting colleagues towork together to take actions that alter a predicted outcome. Thiscollaboration may occur on a common system that enables collaborators toview a legislative bill, discuss the legislative bill, and take actionsthat increase or decrease the likelihood of the bill's passage orlikelihood of any intermediate stages.

While the present disclosure provides examples of collaborative systemsand methods for altering predictive outcomes of dynamic processes,aspects of the disclosure in their broadest sense, are not limited tosteering legislative agendas or proposed legislation. Rather, it iscontemplated that the foregoing principles may be applied to alteringpredictive outcomes for other specific dynamic processes as well. Forexample, the term “specific dynamic process” includes any legislative,regulatory, administrative, judicial, or other related proceedings orprocesses for policymakers. The term “legislative agenda” includes anyideas or set of policymaking that are desired to be made more or lesslikely.

In some embodiments, user collaboration may enable a regulatory agendato be advanced on a common system that enables one or more users to viewa regulatory proposal, discuss the regulatory proposal, and take actionsthat increase the likelihood of the regulatory proposal's passage. Theterm “regulatory agenda” includes any of a set of predeterminedregulatory ideals or regulation that users may aim to advance. In otherembodiments, user collaboration may enable an administrative agenda tobe advanced on a common system that enables administrators to view anadministrative proposal, discuss the administrative proposal, and takeactions that increase the likelihood of a desired administrativeoutcome, for example the administrative proposal's modification, or theadministrative proposals passage. In still other embodiments, usercollaboration may enable a judicial agenda to be advanced on a commonsystem that enables judges to view a proposed judicial decision, discussthe proposed judicial decision, and take actions that increase thelikelihood of the judicial decision. In still other embodiments, usercollaboration may enable a judicial agenda to be advanced on a commonsystem that enables lawyers or individuals seeking legal counsel to viewa proposed judicial decision, discuss the proposed judicial decision,and/or take actions that increase the likelihood of the desired judicialdecision.

The term “outcome” may include a likelihood that legislation isintroduced, assigned to certain legislative committees, recommended outof a certain legislative committee, taken up for consideration on alegislative floor, passes a floor vote, or is ultimately passed andenacted. While the present disclosure provides examples of collaborativesystems and methods for altering predictive outcomes of dynamicprocesses, aspects of the disclosure in their broadest sense, are alsonot limited to any type of predictive outcome. For example, a set ofpossible outcomes may include various stages of legislative, regulatory,administrative, judicial, and other related proceedings or processes arecontemplated.

As discussed earlier, system 100 may comprise a network 101, a pluralityof sources, e.g., source 103 a, 103 b, and 103 c, a central server 105,and user(s) 107. System 100 may allow for information to be scraped fromthe internet. In some embodiments, system 100 may also include devices300, 400 for receiving user input from user(s) 107 collaborating inorder to alter predictive outcomes for dynamic processes.

FIG. 15 is a diagrammatic illustration of a memory 1500 storing modulesand data for altering predictive outcomes of dynamic processes inaccordance with the present disclosure. Memory 1500 may include anapplication or software program providing instructions for collaborationbetween multiple user(s) 107 according to a communication-based dynamicworkflow. The term “communication-based dynamic workflow” includes anyset of instructions or rules that facilitate operational steps accordingto communication between user(s) 107. In some embodiments, memory 1500may be included in, for example, central server 105, discussed above.Further, in other embodiments, the components of memory 1500 may bedistributed over more than one location (e.g., stored in a plurality ofservers in communication with, for example, network 101).

Memory 1500 may include a proceeding information identification module1502, a proceeding position identification module 1504, an actionexecution module 1506, a system user input module 1508, a databaseaccess module 1510, and a database 1512. In some embodiments, systemuser input module 1508 may receive user input or a request from user(s)107 interacting with a graphical user interface (GUI) on devices 300,400. Proceeding information identification module 1502 may thencommunicate with system user input module 1508 to identify the nature ofthe user input or request.

Proceeding information identification module 1502 may then communicatewith action execution module 1506 to take action and executeinstructions to gather information related to the user input or request.For example, action execution module 1506 may instruct database accessmodule 1510 to gather information from database 1512. In still otherembodiments, proceeding position identification module 1504 may analyzethe information procured from the database 1512 in order to provide atendency or likelihood of a favorable outcome. Action execution module1506 may then display this result to user(s) on GUIs displayed ondevices 300, 400. Other types of communication-based dynamic workflowsbetween user(s) are also contemplated.

In further embodiments, and in response to one or more results displayedto the user, user(s) 107 may provide feedback to system user inputmodule 1508 related to their experience with part or all of the system100. The term “feedback” may include communication related to data,analytics on data, mappings, predicted outcomes and their likelihoods,and any other user or system generated feedback. For example, feedbackmay reflect relevance of information or prediction to the user(s), ingeneral or based on a specific query, user priority of the information,user position on the information, correctness, and timeliness.

In other embodiments, feedback may be provided prior to, at the time of,or after the item data is acquired or a prediction is generated. Forexample, user(s) 107 may create and update a profile at any time duringtheir engagement with system 100. The term “profile” may include a setof user preferences, and a preference may be interpreted as feedback.For example, a preference may indicate the type of data the user isinterested in. This indication may be in the form of specific terms thatoccur in documents, broad or specific subject areas which are producedby system 100 via analysis to categorize the document, local or globalmilestones, and policymakers associated with the data, such aslegislators or agencies, and document types.

In some embodiments, a preference may indicate the likelihoods ofpredicted outcomes which the user is interested in. For example, theuser may only want to be notified on any predicted outcomes with a verylow likelihood. A preference may also have or more sub-preferences, ormay constitute a combination of one or more preferences. For example, auser 107 may define his or her own preference to indicate the thresholdat which a predicted outcome likelihood becomes “very low.” A user 107may further have more than one definition of such a likelihood. In someaspects, a different threshold may be set to indicate “very lowlikelihood” for each different piece of analysis, such as predictedpassage of a bill having a different threshold than promulgation of arule.

In other embodiments, a preference may indicate the relationship a firstuser holds to a second user. For example, this may include the type offeedback, models, or other user generated data the first user restricts,or shares.

In some embodiments, both the first and second user may be able tojointly provide feedback, including on existing models, and create newmodels, or any sub-parts. In this example, system 100 may hold a singleversion of the feedback for both users, or hold individual copies offeedback for each user that is then combined to form the combinedfeedback. In other embodiments, the user may distinguish which feedbackwas provided by which user, or feedback may be anonymized such that useris unable to distinguish where the feedback came from.

In some embodiments, generated models successively updated by a firstuser may have versions, each version representing a single piece offeedback, or multiple pieces of feedback. Each version of the model maybe stored and accessed separately. Similarly, feedback provided by thesecond user on the same set of outcomes may be versioned. In otherembodiments, if the first and second user do not share a permissiongroup, and both provide feedback on the same data for the same outcome,their respective feedback may be processed separately and a first userssystem may be unaffected by second users feedback, and the second userssystem may be unaffected by first users feedback.

In some embodiments, if a first user selects to share feedback, models,out other data with second user, second user may see the initialpredicted outcome, a version of the predicted outcome, or first userpredicted outcome and the associated likelihoods. In other embodiments,a preference may also indicate the frequency at which the user should benotified of updates to the data, including new or updated data, seconduser feedback, or new or updated predicted outcomes or likelihoods. Forexample, a first user may provide feedback on the feedback of a seconduser.

In some embodiments, a preference may also indicate the position theuser will take on a given issue. An issue may be a system predefinedsubject area categorization, a user updated system model, or a userspecified issue area, represented as a set of terms, linguisticpatterns, labels, or a user initiated categorization model. A positionmay be an indication of the user's opinion on the issue, such as theissues priority, relevance, importance, or if the user is in support oropposition of the issue. These preferences may be set prior to theacquisition or generation of a target piece of data or analysis, and maybe applied to the target at the time it is acquired, or at any pointlater, such as on user request.

In other embodiments, feedback may include proprietary data, asdiscussed above. For example, feedback may also include feedback onsub-parts of the data or analysis. As a further example, users mayprovide feedback on the predicted outcome of a given model, such astheir agreement with the predicted outcome of a given legislator votingfavorability for a bill. They may also provide feedback on thethresholds used in the model scoring function, such as that aprobability of a sponsor voting yes with less than 70% probability meansthey will vote no.

In other embodiments, users may provide feedback on one or moreparameters used by the model, such as the features used and weighting ofthose features. Users may also provide feedback on an individual featureor weight, or a grouping of features or weights together, where thegrouping may be determined by the user or the system.

For example, a user may indicate that the feature representing thesponsor effectiveness for a specific sponsor of a legislative billshould be removed or that the features representing the sponsoreffectiveness for all sponsors should be removed, or may select aspecific sub-group of sponsors for whom the feature should be removed,or that the sponsor effectiveness features should be removed theirimportance as computed by the model is lower than some threshold.Moreover, the user may indicate that any of those features or groupingsmay be weighted lower by the model.

In some embodiments, a user may indicate that some training instancessuch as documents be given higher or lower importance for computation ofmodel parameters. For example, a user may indicate for subject areacategorization of documents into the “financial” category, documentscontaining the term “angel fund.” Therefore, a feature vector generatedfrom those documents may be weighted lower than documents containing theterm “toxic assets.” The feedback on weighting may be relative, such asa first feature weighted lower than a second feature, or absolute, suchas when a feature should receive a specific weight set by the user. Thefeedback on weighting may completely replace system generated weighting,or may alter it, such as by adding or subtracting some amount. In otherembodiments, relative and absolute weighting may be represented as realnumbers. For example, a first feature may have a weight of 5.3. Thefirst feature may be represented in percentages, and the first featuremay be 50% of the second feature. A first feature may also have a codingscheme comprised of high, medium, and low levels.

In some embodiments, feedback and collaboration may change thecalculation of an existing system defined feature. For example, userfeedback may indicate that the legislator effectiveness score should nottake into account non-substantive legislation. In some embodiments, userfeedback may create new features for a model. For example, a user maycreate an additional feature for the argument recognition model for theoutcome of recognizing if a policy documents presents the argument oflacking time. The feature may be a term, such as “this policy does notprovide enough.” The feature may be associated with a desired outcome bythe user, such that when observed by the system in a policy it isassociated with the outcome of lacking time. If such a feature and theassociated outcome conflicts with any set of features derived by themodel, or the predicted outcome, the user may have a preference of howthe conflict should be resolved. Alternatively, the user created featuremay be left to the system to decide the correlation. The featuresassociated weights may be specified by the user or computed by thesystem. The user created feature may be a term, linguistic pattern,user-defined coding system, etc.

In other embodiments, feedback on the model, such as the predictedoutput, or sub-parts of the model, such as weighting or parameters, mayresult in the model being recomputed. Any feedback, including modifiedfeatures, weights, or new features may be computed by the system on alldata, only for a subset of data or models, including, for example,proprietary data, as indicated by the default profile settings or userprofile settings.

In some embodiments, the re-computation may include removal of an entiretraining instance, such as when the user indicates that part or all ofthe system data was incorrect, removal of features from a traininginstance, changing of weights applied to features of a traininginstance, introducing new training instances, such as from proprietarydata provided by the user, introducing new features into a traininginstances, such as from new features created by the user.

In other embodiments, the model may not be recomputed based on feedback.For instance, after feedback indicating that a feature or group offeatures should be removed, a previously generated model may be applied,while removing those features indicated by the user from the scoringfunction, thus removing their effect on the output, withoutre-computation of the model in a model training phase.

In some embodiments, feedback on a model predicted output or likelihoodmay affect other predicted outputs of the model according to memory1500. For example, feedback indicating one legislator will vote yes on agiven bill, may result in the updating of the predicted outcome andlikelihoods of the other legislators. In some embodiments, this updatemay occur for the predicted votes on the specific data instance, i.e.,bill, on which the feedback was provided. In other embodiments, thisupdate may also affect the predicted outcome (e.g., voting behavior) ofthe user updated legislator on other bills, not directly given feedbackby the user, and other legislators on other bills.

In other embodiments, feedback indicating that a first comment submittedfor a regulation is opposing the regulation, may update the analysis ofa second comment, either on the same regulation or others. For instance,analysis of the second comment may have indicated that the commenteragrees with the position of the first comment. If automated or manualanalysis of the first comment indicated the comment is supporting theregulation, user feedback indicating the comment is in fact opposing theregulation, will update the position of the second comment toopposition.

In some embodiments, feedback on a model predicted output or likelihoodmay affect other models or module 1500, that produce a differentpredicted outcome. For example, user collaboration or feedback on agiven bill's likelihood of enactment may affect the predicted outcomeand likelihood of the model predicting when a rule on the subject willbe promulgated. As a further example, user collaboration or feedback ona given bill's likelihood of enactment may affect the predicted outcomeand likelihood of the model predicting the stance taken by a comment ona regulation.

In other embodiments, feedback on one model may or may not affect othermodels. Users may have the option to enable or restrict their feedbackon one model from being used by other instances of the same model, oranother model. Furthermore, users may have the option to enable orrestrict their feedback from being used as feedback for other usersmodels, whether in their organization or outside of it.

In some embodiments, another type of user feedback may instantiate thecreation of an additional model. The additional model may be intended tocompute one or a set of the same outcomes as existing models generatedby the system, or may be intended to compute a new outcome that waspreviously unavailable by the system. The model may be generated by thesystem, or uploaded by the user. For example, user feedback may specifythat the user wants to create a new model that computes how likely abill is to be enacted. They may upload such a model into the system.Uploading a model may include transfer of data, associated files, etc.either through the network, into a computer, etc. in a specified formatunderstood by the system. The user may instantiate the generation of amodel for a new outcome. In one embodiment, the user may providefeedback to the system by labeling the impact a bill will have on theirorganization. The user labeled data may constitute training data for thecreation of a model, as described above. The system may generate one ormore models with some or all of the users' data as one or more inputs.Instead of using the raw user labeled data, the system may extract oneor more features from the data, as discussed above, to use as inputs forthe model. The system may generate and update these models periodically,e.g., on a schedule, or at the direction of the user. In otherembodiments, a user may provide feedback to the system by labeling auser-defined subject area, such that documents deemed to be within thatsubject area are relevant to the users definition of that issue.

In some embodiments, for either an existing or new outcome, the systemmay select or allow the selection of a set of data, select or allow forthe selection of a set features to be extracted, select or allow for theselection of a or a set of models to be generated, and select or allowfor the selection of associated outcomes, e.g. enactment, impact, issuerelevance, and initiate a model generation phase through the system. Forexample, if a user-defined wanted to create subject area model for“background checks for teen drivers,” a user may select existingdocuments they deem relevant to the issue, select the set, or types offeature they want to extract, for example, phrases occurring in thedocument, select the type of model they want to have, for example acombination of a logistic regression and neural network, and have thesystem generate the model.

In other embodiments, the new model may be updated with additionalfeedback as any existing system model. Users may create new predictedoutcomes from existing system models, or user generated models, or somecombination thereof. Additionally, user generated models may be used bythe system or the user as sub-parts of other models. User feedback,including on the models, sub-parts thereof, or creation of new models,may result in one or more models for each outcome. The user feedback maybe used to generate a new model in a user specific model generationphase, or used to update the existing model, or a combination thereof,where a model may be a combination of the model generated from or updatewith user feedback, and a model generated by the system. For example,there may be multiple models that predict when a regulation will bepromulgated, for example one from the system, one from a user providingfeedback to the system model, and one from the user creating their own.Accordingly, the system may be able to provide users with tailored andmore relevant information based on the users' requests and feedbacks.The system may also combine system acquired and generated data andproprietary data and may update the analytics and predictions to reflectthe combination.

FIG. 16A is a diagrammatic illustration of an exemplary graphical userinterface (GUI) 1600 for user collaboration and feedback consistent withthe disclosure. GUI 1600 may be displayed as part of a screen executedby a software application on, for example, device 300 or device 400. Thescreen may take the form of web browser or web page consistent with thepresent disclosure. Exemplary GUI 1600 may include a system predictedoutcome and likelihood and a user predicted outcome and likelihood basedupon factors (e.g. features) that may be integrated with system 100 ordefined by a user(s) 107. Exemplary GUI 1600 may include “Factor 1,”“Factor 2,” and “Factor 3,” and may allow to “Add a Factor.” GUI 1600may further indicate an amount each factor is contributing to apredicted outcome and likelihood, whether system or used based. Inparticular, a quantitative output may be displayed indicating alikelihood of passage in percentage terms. For example, a systempredicted outcome and likelihood may indicate a “71%” likelihood ofpassage as compared to a user predicted outcome and likelihood of “56%”likelihood of passage according to specified feedback and user setfactors. The feedback factors may update a model, as discussed above, inorder to change a predicted outcome and likelihood.

In some embodiments, feedback may be applied to data in the systemautomatically or manually. For example, a stored user preference may beto increase the likelihood of passage for any bill introduced by aspecific senator to 90%. Another preference could be to weight aspecific factor higher or lower than another specific factor for a givenpredicted outcome. For example, a preference may be to weight theoccurrence of the term “tax liability” higher than the committeeassignment of a bill in the predicted outcome of favorablerecommendation out of the committee. This feedback may be appliedautomatically when the system acquires any bill sponsored by specificsenator, or at some user defined time. User feedback or collaboration(including preferences) may be provided explicitly or implicitly.Explicit feedback may come in the form of user providing feedback thatdirectly affects a specific model. Examples of direct feedback may bemarking a document irrelevant for a specific subject-areacategorization, which may update the subject-area categorization modelto down weight any features associated with the irrelevant document;marking a document as opposing the rule, which may update the stancedetection model to include that comment as part of the training set foropposing comments.

In other embodiments, implicit feedback may come from any interactionthe user has with the system, whether or not it is intended to updateexisting or create new analysis. Implicit feedback may include uploadinga draft document or sharing a document with another user on the systemor publically, which may be used to update existing or create new modelsfor subject-area relevancy categorization, where the terms inside theuploaded or shared document may be deemed relevant to the user.

In some embodiments, a profile may also be established by the system fora user, treating the user settings as predicted outcomes with anassociated model. For example, the system may generate a model with thepredicted outcome of whether the user will take a support or oppositionopinion on an issue. The system may use data derived from a user'sexplicit and implicit feedback to establish this profile. The system mayalso use data derived from a second users feedback, in the usersorganization, or from other users, if the second users permissionsetting allow. In other embodiments, users may have the option to enableor restrict their feedback from being used as data for the establishmentof other system generated user profiles, or other models. Once userfeedback is in the system, it may be saved and be accessible for lateruse, either by the system, or other users. Feedback may be removed fromthe system.

In some embodiments, feedback may include user feedback on the systemgenerated profile. For example, if the system generated profile containsa system generated model for predicting a user's support on an issue,and it predicts incorrectly, the user feedback on the system generatedprofile may include correcting the outcome. Feedback may be generatedand represented in a number of forms such as clicking or selecting anoption to indicate a correction to data or an analytic, input via a textentry, wherein the feedback will be parsed by the system, verbalfeedback input through a microphone, wherein the feedback will be parsedby the system, or through an API, or other interface, etc. The profilemay be represented and generated by a number of means. Users may createand update preferences via a GUI, and users' profiles may be set todefault settings by the system, or preset based. Both the initial systemprediction, and the updated predicted outcome based on user feedback maybe presented to the user. Feedback may be stored by the system in one ormore databases and files. The databases may be segregated from otherdatabases containing non-proprietary data. User generated models ormodels incorporating any user feedback may be stored or run on the samemachines as system derived models or may be stored or run in their ownenvironment.

FIG. 16B is a diagrammatic illustration of a GUI 1610 for usercollaboration and feedback consistent with the present disclosure. GUI1610 may be displayed as part of a screen executed by a softwareapplication on, for example, device 300 or device 400. The screen maytake the form of web browser or web page consistent with the presentdisclosure.

As shown, GUI 1610 may include “My Dashboard” with “Recent Discussions”and “Recent Actions” undertaken for projects or groups, respectively.“Recent Discussions” may serve as a wall posting allowing for commentsmade by a user to be viewed by all users contributing to the project.Date and times of postings may be included. For example, where thedashboard for “Chile Legislation Bills” is selected, user “Crystal Yan”may comment “Did anyone else see the new article in the WSJ” and“@Frankie James” may comment “I added to the news clips section.” Suchexemplary communication allows for collaboration between users andserves as a notification that this task has already been completed so asto avoid unnecessary duplication. Similarly, posting of “Recent Actions”including meetings, fly-ins, committee hearings, and email facilitatesuser collaboration. This is a significant technical improvement overmanual forms of collaboration which are unwieldy when trying tocollaborate over a particular bill.

FIG. 16C is a diagrammatic illustration of a GUI 1620 for usercollaboration. GUI 1620 may be displayed as part of a screen executed bya software application on, for example, device 300 or device 400. Thescreen may take the form of web browser or web page consistent with thepresent disclosure. As shown, GUI 1620 may include a display of“Documents,” “News,” and “Projects” for user collaboration. The“Documents” section may include a link to editable documents for otherusers. The “News” section may include links to relevant news articles.The “Projects” section may include a graphical chart indicating thestatus of projects and whether they are on schedule or behind schedule.GUI 1620 may further facilitate user collaboration and feedbackconsistent with the disclosure.

FIG. 16D is a diagrammatic illustration of a GUI 1630 for usercollaboration. GUI 1630 may be displayed as part of a screen executed bya software application on, for example, device 300 or device 400. Thescreen may take the form of web browser or web page consistent with thepresent disclosure. As shown, GUI 1630 may include a dashboard list of“Projects” that one or more users are collaborating on. “Projects” maycorrespond or correlate exactly to issue agendas, issues, or particularbills. The dashboard may include the “Project Name,” “Lead,” “Labels,”“Impact,” “Status,” and “Last Action.” The dashboard list may behyperlinked and open to other dashboards with additional information.The dashboard may allow for coordination between users and may beupdated electronically to provide real-time status of relevantinformation. GUI 1630 may further facilitate user collaboration andfeedback consistent with the disclosure.

FIG. 16E is a diagrammatic illustration of a GUI 1640 for searching andselecting a legislative bill. GUI 1640 may be displayed as part of ascreen executed by a software application on, for example, device 300 ordevice 400. The screen may take the form of web browser or web pageconsistent with the present disclosure. As shown, GUI 1640 may includesearching according to “Federal” or “State Legislation” and inconjunction with selection of search “Filters” such as “Legislatures,”“Session,” “Status,” “Type,” “Chamber,” “Category,” “Date Range,”“Primary Sponsor,” and “Co-Sponsor.” Exemplary GUI 1640 may also includesearching according to user entry of a textual input search string. Thetextual input search string may include any combination of alphanumericcharacters or Boolean identifiers. A combined search including both useof a textual input search string and “Filters” is contemplated. A searchmay be executed by clicking on a “Search” button. A search may be savedby clicking on “Save this Search.”

Server 105 may extract data from database 1512 according to aninteraction between a system user input module 1508 and a databaseaccess module 1510. Parsing of data based on one or more categories isalso contemplated. The data may be displayed as search results indashboard form. The search results may also be displayed in graphical orform. The “Results” may be sorted by “Relevance” and “Date,” and aselection of “Watch” and “Export” may allow for the user to monitor ortransmit a selected bill to another user for collaboration on the bill.A “Bill Number,” “Session,” “Title,” “Description,” “Last Action,”“Status” and “Outlook” may be displayed for each bill returned as asearch result. The “Outlook” may include the predicted outcome andlikelihood of passage of a bill. A “Results Count Per Legislature” maybe displayed in map form. A “Status Breakdown” indicating when a billwas “Introduced,” “Passed First Chamber,” and “Passed Second Chamber”may also be displayed in graphical or pie-chart form for display to auser. Other user-adjustable data feature settings and variable graphicaldisplays may be contemplated.

In some embodiments, user(s) 107 may desire to collaborate with otherusers in order to advance particular legislation, and the user may entertextual search string “HR” and “351” “Results” are returned. “BillNumber IL HB 678” and “Bill Number WV HB 2518” are displayed at the topof the search results list. Each “Bill Number” may operate as a link toallow for user selection in order to view more details and informationrelated to the selected bill. In some embodiments, clicking of the “BillNumber” instructs server 107 to instruct module 1500 to transition fromexemplary GUI 1640 to a related exemplary GUI to view additional detailspertaining to the selected bill for desired collaboration.

FIG. 16F is a diagrammatic illustration of a GUI 1650 displaying adashboard indicating a likelihood of one or more outcomes related to theselected legislative bill. GUI 1650 may be displayed as part of a screenexecuted by a software application on, for example, device 300 or device400. The screen may take the form of web browser or web page consistentwith the present disclosure. GUI 1650 may include display of “BasicInformation” including “Title,” “Categories,” and “Bill Summary.” “BasicInformation” may include “Chamber and Session Information” such as“Session Dates,” “Session,” and “Chamber,” and may further include“Sponsors and Cosponsors” such as “Primary Sponsors,” “Co-Sponsors,” anda “Breakdown” according to political party, such as “Republican” or“Democrat.”

GUI 1650 may include display of “Documents and Similar Bills” including“Bill Versions” and “Amendments.” GUI 1650 may include an “Outlook”indicating a likelihood of passage based on a calculation. Thecalculation may include “the strength of the bill sponsor,” “thelanguage in the bill,” and “the network of the co-sponsor.” In someembodiments, determining the calculation may include applying modeland/or machine learning, as discussed above. For example, in someembodiments, determining the calculation may include representing thedata as a feature vector, as discussed above. Based on the calculation,a textual result such as “much more likely” or “much less likely” may bedisplayed in accordance with a comparison of the likelihood of passageof similar bills in the same jurisdiction. “Analytics” may include, forexample, a “House Pre-Floor Score,” “House Floor Score,” “SenatePre-Floor Score,” “Senate Floor Score.” Scores may be tabulated anddisplayed in percentage terms. A “Timeline” indicating a “CurrentStatus” of a bill such as “In Senate” may also be displayed to a user.The user may select “Share” to share this with another user or may takeother “Actions” in order to collaborate on this bill and notify otheruser(s) 107.

FIG. 16G is a diagrammatic illustration of a GUI 1660 displaying adashboard indicating tallied votes and a likelihood that the bill willpass into law. GUI 1660 may be displayed as part of a screen executed bya software application on, for example, device 300 or device 400. Thescreen may take the form of web browser or web page consistent with thepresent disclosure. GUI 1660 may include display of “Votes” for multiplepredictive outcomes including a number of votes that “Passed House” anda number of votes to “Advance” bill. “Yes” and “No” votes may displayedin both quantitative textual and graphical form. A user may click thebutton “See All Votes” to see each individual vote cast according toeach legislator. GUI 1660 may further include display of a “ScheduledHearings and Markups” including “Upcoming Events” and “Past Events” maybe displayed.

FIG. 16H is a diagrammatic illustration of a GUI 1670 displaying anexample of a virtual whipboard. GUI 1670 may include informationindicating a number of persons plan to vote “Yes,” “Lean Yes,” “TossUp,” “Lean No,” and “No.” A user may click “Expand” in order to allowfor display of the relevant members of the “House” and “Senate.” GUI1670 may allow a user to “Add Actions” to record an upcoming legislativehearing or event. The “Action” may allow for entry of a “Start Date andTime,” “Action Type,” “Attendees,” a “Link to” the bill, and a“Summary.” The user may click a “Save” button in order to save an“Action.” Other input fields may be contemplated. Further detailsregarding the use of a virtual whipboard are described below.

FIG. 16I is a diagrammatic illustration of a GUI 1680 illustrating atagging capability. A user may create a custom tag (e.g., subject area)related to agenda issues or people by entering a textual tag string suchas “Biosimilars.” The term “Agenda issues” may include topics ofimportance related to a policymaking agenda that a legislator orregulator is attempting to enact. For example, a user may specify areasor “Issue Areas” corresponding to a tag including “Cybersecurity,”“Privacy,” or “Trade.” These may serve as agenda issues that arecritically important to a legislator in order to obtain a “Yes” vote ona bill. In some embodiments, an agenda issue may relate to one or moregovernment bodies. A tag may serve as an alert and a user may “Filter bya tag name” by typing a tag name in an input field or text box. Apriority level such as “High Priority” or “Low Priority” may also beappended with a tag. A custom report may be created by a user selecting“Create Report” in order to create a report confined only to taggedbills. Tagging provides an intuitive way to organize and find relevantlegislative data for collaboration and dissemination to other users.Other tagging feature settings and alerts may be contemplated.

FIG. 17 illustrates a flow chart representing a collaboration predictionmethod 1700 consistent with disclosed embodiments. Steps of method 1700may be performed by one or more processors of a server (e.g., centralserver 105), which may receive data from user(s) 107 regarding thespecific dynamic process and assign or generate likelihood of occurrenceof a potentially differing outcome (e.g. the server may executeproceeding position identification module 1504 and action executionmodule 1506). The term “potentially differing outcome” includes a resultthat differs from a predetermined course due to altering from usercollaboration. Each user may have a different role in an outcome. Theterm “role in an outcome” includes the each user's level of involvementor set of collaborative actions implemented in order to affect theresult.

At step 1702, the server may store initial information about a specificdynamic process. For example, a specific dynamic process may be alegislative proceeding such as an upcoming vote on a “legislative bill”and may be stored in database 1512. The term “legislative proceeding”includes any upcoming vote or stage required to enact or debate aproposed law. In the exemplary embodiment in FIG. 16F, legislative bill“WV HB 2518” illustrates storage of information related to the billincluding its “Title,” “Bill Title,” “Categories,” and “Bill Summary.”“Basic Information” may include “Chamber and Session Information” suchas “Session Dates,” “Session,” and “Chamber,” and may further include“Sponsors and Cosponsors” such as “Primary Sponsors,” “Co-Sponsors,” anda “Breakdown” according to political party, such as “Republican” or“Democrat.” “Documents and Similar Bills” including “Bill Versions” and“Amendments” may be stored. Names and other data pertaining tolegislators may also be stored. This data may also be modified by userinput for updating in a database. Data may also be stored forregulatory, administrative, or judicial proceedings. Stored data mayinclude historical data and/or metadata. Data storage means may includeTeradata, SAS, or other data storage means such as, for example,including Hadoop Data Lake. Other storage means for data sources arecontemplated.

At step 1704, the server may assign to the specific dynamic process afirst likelihood of occurrence of potentially differing outcomes. A“Position Identification Module” 1504 may determine a likelihood that alegislator may vote for a bill or a likelihood that a bill will pass.The assignment of the first likelihood may be based on a calculation.For example, GUI 1640 may include an “Outlook” indicating a likelihoodof passage based on a calculation. The calculation may include “thestrength of the bill sponsor,” “the language in the bill,” and “thenetwork of the co-sponsor.” Based on the calculation, a textual resultor “assignment” such as “much more likely” or “much less likely” may bedisplayed in accordance with a comparison of the likelihood of passageof similar bills in the same jurisdiction. In some embodiments,determining the calculation may include applying a model and/or machinelearning, as discussed above. For example, in some embodiments,determining the calculation may include representing the data as afeature vector, as discussed above.

In some aspects, the server may assign a first likelihood based onscraped data. For example, the server may apply one or more models withsome or all of the scraped data as one or more inputs. Instead of usingraw scraped data, the server may extract one or more features from thedata, as discussed above, to use as inputs for the model.

In some aspects, the data sets may include legislators in a legislativebody associated with a legislative proceeding. In some embodiments, thespecific dynamic process may include at least one of a legislativeproceeding, a regulatory proceeding, or a judicial proceeding.

At step 1706, the server may receive notification data associated withthe specific dynamic process. The term “notification data” includes atleast one of a tag, message, or an alert. In one embodiment, a user maycreate a custom tag related to issues or people by entering a textualtag string. For example, a first user may become aware of the dynamicprocess, and indicate to the system other users that should be alertedto the dynamic process by entering an identifier (e.g. user name, onlinemoniker) for those users in relation to the dynamic process. As afurther example, the user may specify areas or topics corresponding tothe tag including “Issue Areas” such as “Cybersecurity,” “Privacy,” or“Trade.” A tag may serve as an “Alert”, for example when a user may settheir profile settings to be alerted to any dynamic process that istagged by the system, or another user, as related to “Trade”. A user mayalso “Filter by a tag name” by typing a tag name in an input field ortext box. The dynamic process the alert may be related to may be alegislative proceeding such as bill “WV HB 2518” and may indicate thatthe user may be required to take an action, such as attend a hearing. Asa further example, the dynamic process the alert is related to may be aregulatory proceeding and may indicate that a comment needs to bedrafted. In some embodiments, notification data can be generated by theuser on the system at the time of, or after the dynamic process througha GUI. In other embodiments, notification data may be generated by thesystem based on system or user preferences established prior to thedynamic process. For example, user(s) 107 may create and update aprofile at any time during their engagement with system 100. A firstsystem user may specify in their profile that an alert to a second andthird system user should always be generated from a dynamic process thatmeets certain specified criteria (e.g. a regulatory process initiated byFDA about GMO labeling in Montana).

At step 1708, the server may transmit stored initial information aboutthe specific dynamic process to the second system and third system usersbased on the received alert. This transmitting may include sending amessage, creating a report, sending an alert, or exporting policyinformation to another user. At step 1710, method 1700 may includereceiving first additional information responsive to the at least someof the stored initial information about the specific dynamic process andimpacting the specific dynamic process. The first additional informationmay include user feedback, as described above, proprietary data, asdescribed above, metadata such as the name of the sender of the data, ortime the data was sent. Other types of additional information arecontemplated.

At step 1712, the server may generate a second likelihood different fromthe first likelihood. A “Position Identification Module” 1504 maydetermine a second likelihood that a legislator may vote for a bill or alikelihood that a bill will pass. The generation of a second likelihoodmay also be based on a calculation. For example, GUI 1640 may include an“Outlook” indicating a likelihood of passage based on a calculation. Thecalculation may include “the strength of the bill sponsor,” “thelanguage in the bill,” and “the network of the co-sponsor.” Based on thecalculation, for example, the second likelihood may change to “much lesslikely” from “much more likely” based on first additional informationand initial information indicating an increase in a number of “No”votes. In some embodiments, determining the calculation may includeapplying a model and/or machine learning, as discussed above. Forexample, in some embodiments, determining the calculation may includerepresenting the data as a feature vector, as discussed above. As afurther example, the second likelihood of a case being appealed as partof a judicial process may become 68% likely, as opposed to the firstlikelihood of 13% likely, after first additional information from thesecond system user indicates the appeal rate for the given court.

At step 1714, the server may transmit to the first system user, thesecond system user, and the third user an indication of the secondlikelihood. This transmitting may include sending a message, creating areport, sending an alert, or exporting policy information to anotheruser. The transmission may also occur as a numerical or graphicaldisplay available to another user viewing a web browser representativeof an updated GUI consistent with the embodiments in accordance with thepresent disclosure.

At step 1716, the server may receive from the third system user, secondadditional information impacting the specific dynamic process. Thesecond additional information for a legislative process may includetabulation of new votes, and or other metadata associated such as achange in sponsor or co-sponsor of the bill. Other types of additionalinformation in accordance with the disclosure are contemplated.

At step 1718, the server may generate a third likelihood different fromthe first likelihood and the second likelihood. A “PositionIdentification Module” 1504 may determine a third likelihood that alegislator may vote for a bill or a likelihood that a bill will pass.The generation of a third likelihood may also be based on a calculation.For example, exemplary GUI 1640 may include an “Outlook” indicating alikelihood of passage based on a calculation. The calculation mayinclude “the strength of the bill sponsor,” “the language in the bill,”and “the network of the co-sponsor.” Based on the calculation, thesecond likelihood may change to “less likely” from “much less likely”based on information indicating an increase in a number of “No” voteswas not as large an increase as previously predicted. Therefore, whileit appears the bill will still not pass in comparison to other bills,there is less certitude of this outcome. In some embodiments,determining the calculation may include applying a model and/or machinelearning, as discussed above. For example, in some embodiments,determining the calculation may include representing the data as afeature vector, as discussed above.

At step 1720, the server may transmit to the first system user, thesecond system user, and the third system user an indication of the thirdlikelihood of an occurrence of at least one of the potentially differingoutcomes. This transmitting may include sending a message, creating areport, sending an alert, or exporting bill information to another user.The transmission may also occur as a numerical or graphical displayavailable to another user viewing a web browser representative of anupdated GUI consistent with the embodiments in accordance with thepresent disclosure.

Predicting Policymaker Maker Behavior Based on Unrelated Historical Data

In some embodiments, the disclosed systems and methods may involve datapattern analysis for determining patterns in aggregated electronic data.The aggregated electronic data may have been obtained from at least oneInternet server. For example, the disclosed systems and methods mayanalyze the aggregated electronic data to determine patterns and predicthow legislators are likely to act in relation to a particularlegislative bill. The prediction may include any information including aspecific predicted outcome of the particular votes that each legislatorwill cast.

In other embodiments, information that is not necessarily directlyrelated to the subject matter or substance of the pending bill may beanalyzed in order to make a prediction. For example, an influence of alegislator may be determined based on analysis of unrelated informationin order to make a prediction as to how one or more legislators may voteon a prospective legislative bill. The terms “unrelated information” and“unrelated data” include any information that is not related to thesubstance of the legislative bill.

For example, a particular legislator's seniority, prior campaignendorsements, and upcoming election opponent may be “unrelated” to alegislative bill aimed at correcting an existing health care policy.However, this unrelated data may determine the influence of a legislatorin order to predict how one or more legislators may vote on aprospective legislative bill. In still other embodiments, an influenceof a legislator on other legislators may be derived from thelegislator's prior track record, school records, financial situation, orother unrelated historical data.

Aspects of the disclosure in their broadest sense, are not limited topredicting legislator behavior data based on unrelated data to determinea future outcome of a particular legislative bill. Rather, the foregoingprinciples may also be applied to data pattern analysis for determiningpolicymaker patterns, including to predict regulator, administrator,judge, or other related official behavior based on unrelated data inorder to determine corresponding outcomes.

For example, in some embodiments, data pattern analysis may includedetermining patterns and making a prediction about how regulators orpolicymakers are likely to prescribe rules in relation to a particularregulatory or policy proposal. In other embodiments, data patternanalysis may include determining patterns and making a prediction abouthow administrators are likely to administer codes in relation to anadministrative proposal. In still other embodiments, data patternanalysis may include determining patterns and making a prediction abouthow judges are likely to rule or vote in relation to a judicialproceeding or opinion.

In some embodiments, information that is not necessarily directlyrelated to the subject matter or substance of a regulatory, policy,administrative, or judicial proceeding may be analyzed in order to makea prediction. For example, an influence of a regulator may be determinedbased on unrelated information in order to make a prediction as to howone or more regulators may decide on prospective regulation. Aninfluence of a regulator may be derived from the regulator's prior trackrecord, affinity with the current executive, associations with privateorganizations, relationship to other regulators, and from otherunrelated historical data.

In other embodiments, an influence of an administrator may be determinedbased on unrelated information in order to make a prediction as to howone or more administrators may decide on a prospective administrativeproposal. An influence of an administrator may be derived from theadministrator's prior track record and from unrelated historical data.In still other embodiments, an influence of a judge may be determined inorder to make a prediction as to how one or more judges may vote on aprospective judicial ruling. An influence of a judge may be derived fromthe judge's prior track record, citations patterns, writing style,previous legal career, and from unrelated historical data. It iscontemplated that other types of data and data analysis based onunrelated information are available to determine influence and theoutcome of any prospective or future event.

As discussed above, system 100 may comprise network 101, plurality ofsources, e.g., source 103 a, 103 b, and 103 c, central server 105, anduser(s) 107. Consistent with this disclosure, system 100 may includedevices 300, 400 for receiving requests to identify prospective futureevents related to predefined subject matter, and for predicting theoutcome of the future events based on patterns in data for portionsunrelated to the subject matter.

The term “prospective future event” or “future event” includes any ofanticipated activities that may occur in the near or distant future. Forexample, the future event may include both final and intermediate stagesof a disposition, such as bringing a vote to a legislative floor, orenacting a legislative bill. Other future events are contemplated. Theterm “predefined subject matter” includes any of predetermined topics orcategories that may constitute the substance of a legislative bill orproceeding. For example, the topic of income taxes may be set as apredefined subject matter that occupies the substance of a billproposing an income tax cut or tax increase. Further, in someembodiments, predefined subject matter may include information includingportions unrelated to the subject matter of the at least one prospectivefuture event. The term “portions unrelated to the subject matter”includes any information that is not related to the substance of thelegislative bill.

FIG. 18 is a diagrammatic illustration of a memory 1800 storing modulesand data for performing internet-based data pattern analysis. Inparticular, as shown, memory 1800 may include an informationidentification module 1802, an outcome identification module 1804, anaction execution module 1806, a system user input module 1808, adatabase access module 1810, and a database 1812. In some embodiments,memory 1800 may be included in, for example, central server 105,discussed above. Further, in other embodiments, the components of memory1800 may be distributed over more than one location (e.g., stored in aplurality of servers in communication with, for example, network 101).

In some embodiments, system user input module 1808 may receive a request(e.g., from a user device) to identify a prospective future eventrelated to predefined subject matter. The request and the predefinedsubject matter may be communicated by user input. Action executionmodule 1806 may then communicate with database access module 1810 tosearch database 1812 for information scraped from Internet sources 103a, 103 b, and 103 c that is both related and unrelated to predefinedsubject matter. This information may identify individuals orpolicymakers with a role in the outcome of the future event.

In some embodiments, information identification module 1802 maydetermine whether or not the data is related to the predefined subjectmatter of the prospective future event. In other embodiments, outcomeidentification module 1804 may determine an influence factor for each ofthe identified individuals based on patterns in data unrelated to thepredefined subject matter. The term “influence factor” includes theextent or magnitude to which a policymaker will affect the outcome ofother policymakers. In still other embodiments, outcome module 1804 maypredict the outcome of a future event based on patterns analyzed of dataunrelated to the predefined subject matter. Action execution module 1806may then display the outcome of a future event to one or more user(s)107 operating devices 300, 400.

The following discussion pertains to examples of GUIs that may displaydata to a user of system 100. The GUIs may be displayed on a screenassociated with a user device, such as device 300 or device 400,discussed above.

FIG. 19A is a diagrammatic illustration of a GUI 1900 for viewingunrelated legislator or other public official data. As shown, GUI 1900may include “Basic Information” such as a “Title,” “Party,” “District,”“Email,” “Phone,” “Address,” and “Biography” unrelated to a subjectmatter of the bill. “Contacts and Staff” may include a listing ofmultiple office addresses and names of staff members that work for thelegislator. This information may be significantly unrelated topredefined subject matter of a bill but may affect its outcome. Forexample, the influence of staff members may but unrelated but mayprovide significant influence to a bill's passage. GUI 1900 may furtherinclude a toolbar to list the “Chamber,” “Reelection Year,” and “AverageBills Introduced” for each legislator. Other unrelated informationrelating to an identity, identities, or track record of officials, butnot directly related to a policy or legislative proceeding is herebycontemplated.

FIG. 19B is a diagrammatic illustration of a GUI 1910 for viewingadditional unrelated legislator or other public official data. As shown,GUI 1910 may include data related to particular committees,subcommittees, or caucuses. A user may select a “Committees” tab or a“Caucuses” tab in order to view more information about the legislator'sparticipation. For example, legislator “Del. Joe Smith (R)” is a memberof 11 committees including the “House Committee on Health and HumanResources,” “Joint Committee on Health,” “Joint Interim LegislativeOversight Commission on Health and Human Resources Accountability,” and“House Select Committee on Prevention and Treatment of Substance Abuse.”For each committee, Del. Joe Smith occupies the “Position: Chair.” Theterm “position” includes an occupied status, but may also include aparticular stance or inclination of a legislator.

GUI 1910 may further include a list of legislators that are similar toDel. Smith. The list may include the “Name,” “Chamber,” “District,” and“Voting,” and “Co-sponsorship” listed in percentage or frequency terms.This foregoing information may or may not be related to the predefinedsubject matter of a bill. For example, committee membership on the“Joint Committee on Health” may act as unrelated information relative toa bill related to tax policy. However, it may inform an influence factorif certain legislators are members of both the “Joint Committee onHealth” and other committees related to tax policy.

FIG. 19C is a diagrammatic illustration of a GUI 1920 for viewingadditional unrelated legislator or other public official data. GUI 1920may include a number and list of “Sponsored Bills.” These lists includebills that are sponsored by the legislator. GUI 1920 may further includea graphical display of “Top Policy Issues” for the legislator. Forexample, an “Effectiveness as a Primary Sponsor” metric may becalculated and displayed in graphical form and may be categorized bypolicy type. For example, Del. Joe Smith has a score of 70 out of 100relating to “Civil Rights” effectiveness and a score of 80 out of 100relating to “Food and Beverage” effectiveness when a primary sponsor ofa related bill. Therefore, Del. Smith more effective sponsoring bill'srelating to “Food and Beverage” than to relating to “Civil Rights.”

Effectiveness may also be compared to a “Chamber Average.” For example,relating to “Families and Children,” Del. Joe Smith has a score of 73.8compared to the “Chamber Average.” of 45.8. Accordingly, Del. Joe Smithis more effective than the average legislator in his chamber as servingas a primary sponsor of “Families and Children” legislation. Aggregateddata relating to effectiveness may also be determined. For example,“Del. Smith is in the top 16% of the WV House” and is “very effective asa primary sponsor.” Therefore, Del. Joe Smith has a high “influencefactor” since Joe is effective in influencing others to passlegislation.

FIG. 19D is a diagrammatic illustration of a GUI 1930 for viewingadditional unrelated legislator or other public official data. GUI 1930may include “Watched Bills,” allowing for a user to “Add Actions,” and agraphic display or assessment of a legislator's “Ideology.” The ideologymay be graphically and quantitatively displayed based on legislationtype for “All Sessions” or only based on “Current Sessions.” A“legislator” may be ranked or categorized as “Liberal,” “Moderate,” or“Conservative.” For example, Del. Smith is ranked aggregately as “fairlyconservative overall.” This information may relate to the voting recordand may inform future voting predictions for the legislator depending onthe nature of the predefined subject matter.

FIG. 19E is a diagrammatic illustration of a further example of a GUI1940 for viewing an outcome based on unrelated data. GUI 1940 may bedisplayed as part of a screen executed by a software application on, forexample, device 300 or device 400. The screen may take the form of a webbrowser or web page consistent with the present disclosure. ExemplaryGUI 1940 may include a predicted outcome, including for example,“Compared to other bills in California, this bill is less likely topass.” This predicted outcome may be calculated based upon unrelateddata such as “the strength of the bill sponsor” and “the network of thecosponsors.” The predicted outcome may also be based on related datasuch as “the language in the bill.” Based upon a combination ofunrelated and related data, a “Recommended Action” may be presented to auser. For example, the recommendation may include “finding additionalsenior cosponsors on the referred committee to increase the likelihoodof passage.” Other predictions may be presented as part of exemplary GUI1940 in accordance with the predicted outcome based upon unrelated data.

FIG. 20 illustrates an example flow chart representing an internet-baseddata pattern analysis method 2000 consistent with disclosed embodiments.Steps of method 2000 may be performed by one or more processors of aserver (e.g., central server 105), which may receive data from user(s)107 requesting to identify at least one prospective future event relatedto a predefined subject matter.

At step 2002, the server may receive a request to identify at least oneprospective future event related to a predefined subject matter. Therequest may be based on user input entered into GUI 1910 (e.g., beingdisplay on a display screen or display of a user device) and may be sentfrom central server 105 or from users 107. The at least one prospectivefuture event may include at least one policymaking proceeding. The term“policymaking proceeding” may include any activity where the merits of apolicy are determined. For example, a policymaking proceeding mayinclude at least one of a legislative vote or regulatory vote forrulemaking. For example, a specific bill in a West Virginia statelegislature relating to the predefined subject matter of families andchildren may be scheduled for an upcoming vote. Accordingly, a user 107may request to identify this vote.

In some embodiments, a prospective future event may include at least onepolicymaking proceeding. For example, system 100 may include informationthat identifies individuals with a role in an outcome of the at leastone prospective future event to include identities of officials slatedto participate in the policymaking proceeding. In other embodiments,system 100 may include information that identifies individuals with arole in an outcome of at least one prospective future event by includingpolicy position data of policymakers. The term “policy position data”includes information that indicates a particular stance of a policymakeras it relates to a public policy.

In some embodiments, system 100 may include a prospective future eventthat includes at least one legislative proceeding. System 100 mayinclude the information that identifies individuals with a role in anoutcome of at least one prospective future event to include identitiesof legislators slated to cast a vote in the legislative proceeding. Inother embodiments, system 100 may include information that identifiesindividuals with a role in an outcome of the at least one prospectivefuture event to include voting record data of policymakers.

At step 2004, the server may access and ingest data scraped from theInternet to identify information that identifies individuals with a rolein an outcome of the at least one prospective future event related to apredefined subject matter. The data may constitute the identities ofofficials slated to participate in the policymaking proceeding andpolicy position data of policymakers. The data may further includevoting record data of policymakers scraped by extracting or parsing HTMLcontent or other textual content as discussed in earlier sections. Thedata may be ingested and aggregated for display to one or more users107. This data may be related or unrelated to the predefined subjectmatter and may be displayed as part of the GUIs shown in FIGS. 19A-19E.For example, the scraping may include scraping data from a West Virginialegislative website or from a Wiki source.

At step 2006, the server may process the information to determinepatterns in portions of the data unrelated to the predefined subjectmatter. As discussed above, the processing may include operationsaccording to instructions from central server 105, and in accordancewith module 1800 as described here in. Information identification module1802 may determine whether or not the data is related to the predefinedsubject matter of the prospective future event, and outcomeidentification module 1804 may determine whether patterns exist inportions of the data unrelated to the predefined subject matter.Processing information may further include processing about the priorsuccesses and failures of the identified policymakers in achieving priorunrelated outcomes in accordance with the disclosure.

At step 2008, the server may determine an influence factor for each ofthe identified individuals. The influence factor may be calculated byoutcome identification module 1804 based on a determined effectivenessof a legislator. Calculation may occur according to one or morepreferred models and based on application of analysis as discussed inprior sections. For example, an “Effectiveness as a Primary Sponsor”metric may be calculated and displayed in graphical form and may becategorized by policy type to indicate the amount of influence apolicymaker has when acting as a primary sponsor of a bill. Otherfactors such as ideology, past voting records, and participation incommittees, subcommittees, and caucuses may determine influence. Suchinfluence may also serve as a metric to indicate ability to pursue afurther higher elective office or to fundraise or raise money. Othertypes of influence, influence factors, and thresholds based on unrelateddata to a predefined subject matter are contemplated.

At step 2010, the server may predict a future event outcome andlikelihood according to outcome identification module 1804 and based onpatterns analyzed in data unrelated to the predefined subject matter.Algorithms or models may be used in accordance with the disclosure maydetermine the extent to which unrelated data may affect an outcome of afuture proceeding such as a legislative vote.

For example, system 100 may include information including portionsunrelated to the subject matter of the at least one prospective futureevent such as at least one of a campaign contribution, campaignendorsement, and upcoming election. Other types of unrelated subjectmatter as well as establishing a threshold between unrelated and relateddata relative to predefined subject matter is contemplated. For example,a threshold value may be preset and data may be quantified to indicate alevel of directness relative to the threshold value. In particular, dataquantified at a level higher than the threshold value may be categorizedas related data, and data quantified at a level lower than the thresholdmay value may be categorized as unrelated data. Accordingly, a user maymodify or alter in real-time what may constitute related or unrelateddata based on manipulation of a threshold value between data portionsand a prospective future event. In some embodiments, the unrelated datamay include proprietary information.

Analyzing Policymaker Alignment with Organizational Posture

In some embodiments, the disclosed systems and methods may involveinternet-based agenda data analysis. For example, the disclosed systemsand methods may enable one or more organizations to determine howpolicymakers align with the organizations' legislative, regulatory, orjudicial postures. Organizations may weigh issues that are of interestto them, and based on the weighing, a user interface may reveal, in agraphical format, the relative alignment of legislators to theorganizations' legislative posture.

Aspects of the disclosure, in their broadest sense, are not limited toan issue-based analysis of legislator alignment with an organizationalposture. Rather, it is contemplated that the foregoing principles may beapplied to enable one or more organizations to determine howpolicymakers, including regulators, administrators, judges, and otherrelated officials align with the organization's corresponding postures.The term “organizational posture” includes the particular stance orpolitical position of an organization. The term “organization” includesany collection of individuals operating according to a common purpose,as described above.

For example, in some embodiments, internet-based agenda data analysismay enable determination of legislator alignment with an organization'slegislative posture. In other embodiments, internet-based agenda dataanalysis may enable determination of administrator alignment with anorganization's administrative posture. In still other embodiments,internet-based agenda data analysis may enable determination of judicialalignment with an organization's judicial posture. Internet-based agendadata analysis may be performed to determine alignment for anypolicymaker with any of an organization's related postures.

As discussed above, system 100 may comprise network 101, plurality ofsources, e.g., source 103 a, 103 b, and 103 c, central server 105, anduser(s) 107. Consistent with the disclosure, system 100 may includedevices 300, 400 for receiving user input from user(s) 107 and fordisplaying the alignment position of multiple policymakers relative toan organizational or user posture. The term “alignment position”includes a measure of how policymakers are oriented relative to anorganizational or user posture. In some embodiments, the alignmentposition of a policymaker may be based on one or more positions of aplurality of organizations or at least two organizations.

FIG. 21 is a diagrammatic illustration of a memory 2100 storing modulesand data for performing internet-based agenda data analysis and, inparticular, for performing an issue-based analysis of a policymakeralignment with an organizational posture. Memory 2100 may include aninformation identification module 2102, an alignment identificationmodule 2104, an action execution module 2106, a system user input module2108, a database access module 2110, and a database 2112. In someembodiments, memory 2100 may be included in, for example, central server105, discussed above. Further, in other embodiments, the components ofmemory 2100 may be distributed over more than one location (e.g., storedin a plurality of servers in communication with, for example, network101).

Based on a selection of user-selectable agendas from a list, system userinput module 2108 may receive agenda issues of interest to anorganization. Information identification module 2102 may then identifyan indication of an organization's position or posture on each selectedissue. Memory 2100 may further instruct database access module 2110 tosearch database 2112 for policymaker data from which an alignmentposition on each of the agenda issues is determinable. In some aspects,if policymaker data is not available, action execution module 2106 mayscrape the Internet in accordance with the disclosure to determineindividual policymaker data including one or more alignment positions.

In some embodiments, alignment identification module 2104 may calculatean alignment position data from individual policymaker data. In someaspects, the alignment position data may correspond to relativepositions of each of the plurality of policymakers on the plurality ofselected issues. In other embodiments, action execution module 2106 maytransform the alignment position data into a graphical display thatpresents the alignment positions of multiple policymakers relative to anorganizational or user posture.

In further embodiments, memory 2100 may store instructions for performinternet-based agenda data analysis to identify how actors includingpolicymakers (e.g. legislators, regulators), and other users ororganizations align with a user's position. A rating system may beproduced which indicates an alignment score for each selected actor, andmay be used to rate each actor relative to other actors. In thisexample, a higher score may indicate a greater alignment between theuser and actor. The rating system or any other mechanism of establishingalignment may further be used to suggest policymakers for a user tocontact, for the purpose of making a financial contribution, forproposing a bill that will benefit the user and have the highestlikelihood of bill introduction and passage, for selecting thejurisdiction for a judicial proceeding, or for coordination with asecond user or organization that shares aspects of the first users' ororganizations' posture.

In some embodiments alignment identification module 2104 may predict anindication of an organization's position on a selected issue. Forexample, alignment identification module 2104 may apply one or moremodels to data related to the organization to determine a prediction ofone or more possible positions of an organization and may also determinea likelihood that the organization will in fact have a particularpredicted position (e.g., a confidence score expressed, for example, asa percentage).

In other embodiments, identifying a legislator's position or posture onan issue may be determined by examining a legislator's previous history,including voting behavior, sponsoring behavior, statements, receivedfinancial contributions, and other information. In still otherembodiments, identifying a legislator's position or posture on an issuemay be determined by other data. Such data may be proprietary data,including private conversations, in person, phone, or through othertechnology enabled means: email, IMS, etc. This data may be ingested bythe system and may be used to establish the legislator's positions.

In addition to identifying legislator's posture, in still otherembodiments, a user of system 100 (i.e. non-policymaker) may have aposition or posture on an issue that may be established by the user. Forexample, this may be established in a user profile, or through aninternet-based agenda data analysis system, via automated analysis ofthe user's feedback. For example, the system may automatically identifybills where the user has indicated support, and where legislators votedin support of the bill. In some aspects, if no vote data is present, apredicted outcome, i.e., the likelihood of the legislator voting insupport of the bill, may be entered or used in place of a reallegislator vote to identify bills on which both the user (i.e. anon-policymaker) and the legislator may agree.

In some embodiments, if the user has uploaded additional proprietarydata indicating legislators' positions, a model may be generated topredict legislator positions using the uploaded data. Thisuser-generated model may be used in addition to, in place of, or incombination with any system models to predict legislators' positions,and to compute agenda issue agreement between the user and a legislator.Furthermore, if the user has not indicated that they support a givenbill, a system-generated or user-generated model for predicting the userposition may be used in place of the explicit user position on a bill,in conjunction with a real or predicted vote or position, to identifybills on which the user and the legislator agree. In other embodiments,a first user's indicated or system-generated or user-generated predictedpositions and a second user's indicated or system-generated oruser-generated predicted positions may be used to compute alignment.

In other embodiments, an internet-based agenda data analysis may performan ideology analysis to identify a legislator's position on an issue. Auser may be projected to occupy the same ideology as the legislatorsaccording to an ideology analysis or ideology model, and real orpredicted user positions. In some aspects, a rating system may beapplied to actors that are other users, or non-user organizations. Therating system may connect users that share similar positions, in orderto form a coalition with and drive a similar agenda, or to shareresources. In other aspects, similar users may choose to establish apermission group that may allow sharing feedback to the system, asdescribed above.

In some embodiments, one or more organizational positions or posturesmay contribute to rating uniformly, or there may be a system or userspecified weighing that weights the contribution of organizationalpositions differently. A weighing may include weighing single positionsor groups of positions. Weighing of groups of positions may include somepositions pertaining to particular issues weighted higher than otherpositions. Other weighing may apply to specific bill, where thealignment of position on that bill may contribute to the overallalignment rating differently than other bills. Other weighing may applyto specific regulation, where the alignment of position on thatregulation may contribute to the overall alignment rating differentlythan other regulations. Other types of weighing are contemplated inaccordance with the present disclosure.

In other embodiments, the weighing system may be accessed through a GUI,where a user may select items and their associated weight. The ratingmay be presented to the user as a list, where a user can select anordering, and see names and information relating to policymakers, alongwith associated scores ranking the policymakers.

In some embodiments, a visual graph may be displayed to a user andinclude alignment coordinates to represent a policymaker's position onone or more agenda issues. In some aspects, this graph may be amulti-dimensional presentation, where one axis constitutes an alignmentrating between the user and a legislator, and a second axis constitutesan effectiveness of the legislators, or how much money the user hascontributed to the legislator. In other aspects, the graph may includeone axis representing user favorability toward one agenda issue andanother axis representing user favorability toward a different agendaissue.

In still other aspects, the user may overlay the alignment rating systemon a whipboard predicting the likelihood that legislators will vote fora given bill. For example, users may select a group of legislators, ascan be defined by the grouping in the whipboard, and direct atcommunication at those legislators. In further aspects, the first axismay represent the alignment between the first user and other users, andthe second axis may represent the geographical distance between users'locations. Other types of graphical display may be contemplated.

FIG. 22A is diagrammatic illustration of a GUI 2200 presenting a list ofuser-selectable agenda issues for performing internet-based agenda dataanalysis. In particular, FIG. 22A illustrates an exemplary GUIpresenting a list of user-selectable agenda issues for performing anissue-based analysis of legislature alignment with organizationalposture. GUI 2200 may be displayed as part of a screen executed by asoftware application on, for example, a personal device 300, 400. Thescreen may take the form of a web browser or web page consistent withthe present disclosure.

GUI 2200 may include an “Issue Board” that aggregates essentialinformation around issues in one integrated dashboard. The dashboard mayallow for updates in real-time based on input or control to adjustweighting of each user-selected agenda issue. Agenda issues may includelegislative agenda issues, regulatory agenda issues, and judicial agendaissues. A legislative agenda issue may be “Renewable Energy,” aregulatory agenda issue may be “Net Neutrality,” and a judicial agendaissue may be “Countervailing Duties”. The “Issue Board” may be segmentedbased on issues or geography, and may also be customizable. A user maybe able to “Create Issue” or “Search Issues,” and may be able to filterissues that are displayed. For example, exemplary GUI 2200 may filter orsegment agenda issues according to “Lead,” “Jurisdiction,” “Issue Type,”“Priority,” “Engagement,” “Policy Areas,” “Impact,” and “Status” inorder to limit display to particular agenda issues. In some aspects,only “Issue Areas” relating to “Cybersecurity” and “Privacy” may bedisplayed. GUI 2200 may further include a “Weekly Summary Update” and“Executive Summary” or a list of user-selectable agenda issues that arepresented to a user via a user interface. A “Weighting” and a “DesiredOutcome” may be displayed. Agenda issues may be hyperlinked to allow foruser selection. Upon selection of agenda issue, one more users may enteran indication of an organization's posture for the selected issue.User(s) 107 may select agenda issues and input organizational positionor posture information to determine policymaker alignment relative tothe selected agenda issues.

FIG. 22B is diagrammatic illustration of a GUI 2210 presenting adashboard of alignment of a legislators position on bills with theusers. In particular, FIG. 22B illustrates an exemplary GUI 2210dashboard including headers such as a “Bill Number,” “Session,”“Priority,” “Position,” “Vote,” and “Alignment with Me.” There may benumerous indicators or drop down menus for users to select a “Priority”and “Position.” Based on a vote and other data, a calculation based onanalysis may take place in accordance with the present disclosure inorder to determine whether there is alignment with the user viewing thedashboard. “Alignment with Me” indicates whether a position defined in aparticular bill aligns with the user or organization operating thedashboard.

FIG. 22C is diagrammatic illustration of a GUI 2220 presenting adashboard where sector weights may be adjusted in order to provide aweighted score for each issue area. For example, GUI 2220 may includesections to “Adjust Section Weights,” “Select States to Filter Tables,”“Vote Breakdown by Bill,” and “Select Bars to Filter Tables.” GUI 2220further may present a “Weighted Score by Legislator.” A user may weighsectors or issues that are deemed most critical to the user byadjusting, clicking, or dragging a toggle. In accordance with thetoggling of weight, display of other selections may be altered. In someaspects, a user may select and filter in order to allow for display ofonly desired information. Desired information may be displayed ingraphical, tabular, and numerical form. Other types of display arecontemplated. In this example, the user has weighted “Energy” at “13%”and as a result a weighted score for each legislator may be computed byalignment identification module 2104 and displayed.

FIG. 22D is a diagrammatic illustration of a GUI 2220 presenting agraphical display that includes alignment coordinates displayed ingraphical form. Each displayed coordinate may represent a singlepolicymaker's position on a single issue. In some embodiments, afteralignment coordinates are displayed in graphical form, a user may adjustrelative positions of the coordinates based on subsequent usermanipulation of the at least one weighting control. The term “usermanipulation” includes any modification by clicking, selecting, ordragging the weighting control in order to change alignment coordinatesfor a legislator. The term “control” includes any weighting based onuser input. In other embodiments, after alignment coordinates aredisplayed in graphical form, a user may subsequently access updatedindividual policymaker data, and adjust relative positions of thecoordinates based on updated individual policymaker data.

In still other embodiments, each displayed coordinate may beinteractive, enabling a user who engages with the coordinate to viewpolicymaker information. GUI 2230 may include the policymakerinformation that includes an identity of each policymaker. For example,the names of legislators and other legislators are displayed. On oneaxis, “vote with you” and “vote against you” based on a weighted scoreis displayed. On the other axis, ideology include liberal andconservative is presented. Liberals legislators are displayed are on theleft and more conservative conservatives are on the far right.Accordingly, four quadrants may be displayed including “Liberals thattend to vote with you” in the top left, “Liberals that that tend to voteagainst you” in the bottom left, “Conservatives that tend to vote withyou” in the top right, and “Conservatives that tend to vote against you”in the bottom right. Other alignment coordinates quadrants, axes, anddisplays are contemplated. Exemplary GUI 2230 allows for display of analignment of multiple policymakers relative to a weighted organizationalposture.

FIG. 23A illustrates an example flow chart representing aninternet-based agenda data analysis method 2300 consistent withdisclosed embodiments. Steps of method 2300 may be performed by one ormore processors of a server (e.g., central server 105), which mayreceive data from user(s) 107 selecting both agenda issues of interestand an indication of an organization's position, and subsequentlypresent alignment position data to user(s) 107 based on the selection.

At step 2302, the server may maintain a list of user-selectable agendaissues. The list of user-selectable agenda issues may be stored inmodular database 2112, one or more storage servers 205 a and 205 b, andas part of sources 103 a, 103 b, and 103 c comprising one or more localdatabases. The list of user-selectable agenda issues may be periodicallyupdated, added, and deleted based on user input received at user inputmodule 2108. User-selectable agenda issues may comprise any topic orsubject matter as relevant to an organization, and may be specific orbroad in scope. For example, as illustrated in FIG. 22A, user-selectableagenda issues may include “EU Privacy Direct 2016/680,” “TTIP,” “EUDirective on Cybersecurity” and may be related to issue areas such as“Cybersecurity,” “Privacy,” “Trade,” or government bodies, such as NewYork, China or Brazil. Other agenda user-selectable agenda issuescorresponding to other issue areas may be contemplated.

At step 2304, the server may present to a user via a user interface, thelist of user-selectable agenda issues. For example, as illustrated inFIG. 22A, the list may be presented as part of an exemplary GUI 2200constituting an “Issue Board.” The “Issue Board” may aggregate allpertinent information relating to the list of user-selectable agendaissues in one consolidated dashboard. The list of user-selectable agendaissues and related information may be presented in tabular form and mayinclude hyperlinks to allow for user selection and modification ofagenda issues and related information. In some embodiments, the servermay present to the user at least one control to adjust weighting of eachuser-selected agenda issue, wherein the weighting constitutes anorganizational posture reflecting an overall stance of the organization.For example, “Weighting” may include “High,” “Medium,” and “Low.”However, other more precise and quantitative controls to adjust weighingof each user-selected agenda issue may be envisioned. The term “overallstance” includes the aggregate or summary of final position of anorganization as it relates to a particular item.

At step 2306, the server may receive agenda issues of interest to anorganization. User-selectable agenda issues may be selected by user(s)107 and received at user input module 2108. In particular, based on aselection of user-selectable agendas from a list, system user inputmodule 2108 may receive agenda issues of interest to an organization. Insome embodiments, at step 2308, the server may receive an indication ofthe organization's position or posture on each selected issue.Information identification module 2102 may identify an indication of anorganization's position or posture on each selected issue.

At step 2310, the server may determine an alignment position of eachpolicymaker for each of the agenda issues. Memory 2100 may furtherinstruct database access module 2110 to search database 2112 forpolicymaker data from which an alignment position on each of the agendaissues is determinable. In some aspects, if policymaker data is notavailable, action execution module 2106 may scrape the Internet todetermine individual policymaker data including one or more alignmentpositions.

At step 2312, the server may calculate alignment position data from theindividual policymaker data according to models in accordance with thepresent disclosure. The policymaker information may include an identityof each policymaker. The policymaker information includes at least oneof voting history information of each of the legislators and partyaffiliation of each of the legislators. The policymaker information mayalso include at least one of regulation information of each of theregulators or government officials and party affiliation of each of theregulators or government officials. The policymaker information may alsoinclude at least one of voting history information of each of the eachof the judges and nomination information or electorate demographics ofeach of the judges. Alignment identification module 2104 may calculatean alignment position data from individual policymaker data. In someembodiments, it may further aggregate alignment position of policymakersto compare a first plurality of policymakers to a second plurality ofpolicymakers (e.g. the organizational posture to China's posture andBrazil's posture.) In some aspects, the alignment position data maycorrespond to relative positions of each of the plurality ofpolicymakers on the plurality of selected issues.

At step 2314, the server may transform the alignment position data intoa graphical display that presents the alignment positions of multiplepolicymakers. Action execution module 2106 may transform the alignmentposition data into a graphical display that presents the alignmentpositions of multiple policymakers. After the alignment coordinates aredisplayed in graphical form, a user may adjust relative positions of thecoordinates based on subsequent user manipulation of the at least oneweighting control. After the alignment coordinates are displayed ingraphical form, a user may subsequently access updated individualpolicymaker data, and to adjust relative positions of the coordinatesbased on updated individual policymaker data as illustrated in FIGS.22C-22D. Display in graphical form may provide useful information todetermine legislator alignment with a predefined organizational posture.

FIG. 23B illustrates an example flow chart representing a secondinternet-based agenda data analysis method 2300 consistent withdisclosed embodiments. Steps of process 2320 may be performed by one ormore processors of a server (e.g., central server 105), which mayreceive data from user(s) 107 selecting both agenda issues of interestand, in some embodiments, an indication of an organization's position,and subsequently present alignment position data to user(s) 107 based onthe selection.

At step 2322, the server may receive policymaker data. Policymaker datamay be submitted by user(s) 107 and received at user input module 2108.At step 2324, the server may compute a policymaker position on an issuein accordance with the prior method. The computation may derive from theindividual policymaker data. The policymaker information may include anidentity of each policymaker. The policymaker information may furtherinclude at least one of voting history information of each of thelegislators and party affiliation of each of the legislators. Thepolicymaker information may also include at least one of regulationinformation of each of the regulators or government officials and partyaffiliation of each of the regulators or government officials. Alignmentidentification module 2104 may calculate an alignment position data fromindividual policymaker data to determine a policymaker position on anissue in accordance with the disclosure.

At step 2326, the server may receive user proprietary data. User datamay be submitted by user(s) 107 and received at user input module 2108.At step 2328, the server may compute a user position on an issueaccording to models, as discussed in earlier sections. The userinformation may include an identity of each user. The user informationmay include at least one of user activity of each of the users.Alignment identification module 2104 may calculate an alignment positiondata from user data to determine a user's position on an issue. This maybe display as shown in FIGS. 22B and 22D.

At step 2330, the server may compute alignment of a user to apolicymaker. Alignment identification module 2104 may calculate analignment of a user to a policymaker. At step 2332, the server may rankpolicymakers according to alignment. Alignment identification module2104 may rank policymakers according to alignment. A policymaker with aclosest alignment to a user may receive a highest score, and apolicymaker with the furthest alignment may receive a lowest score.Ranking may be weighted and displayed as part of a “Weighted Score ByLegislator” as shown in FIG. 22C. Other types of ranking and display arecontemplated.

Virtual Whipboard

As described in more detail below, the disclosed embodiments may includesystems and methods for generating and displaying a virtual whipboard toa system user. For example, in one embodiment, the virtual whipboard mayinclude one or more groupings of legislators slated to vote on a pendinglegislative bill. The groupings may be based on any desired category,such as legislators likely to place a similar vote (e.g., affirmative,negative, leaning affirmative, leaning negative, etc.) on the pendinglegislation. An example of a graphical user interface for a virtualwhipboard was discussed above in connection with FIG. 16H.

Further, in some embodiments, the virtual whipboard may include anintegrated communications interface to enable the user to generate oneor more communications targeted at one or more legislators slated tovote on the pending legislation. In some embodiments, the system mayallow a user to communicate directly from the virtual whipboard with aselected legislator or group of legislators. This may enable, forexample, a user to send a common message to personnel associated withlegislators who have an aligned position on a particular bill. In someembodiments, one or more of the foregoing features may enable the systemuser to contact one or more legislators in a more convenient manner thanexisting systems allow.

FIG. 24 is a diagram illustrating a memory 2400 storing a plurality ofmodules. The modules may be executable by one or more processors of aserver (e.g., central server 105, discussed) above. Further, in otherembodiments, the components of memory 2400 may be distributed over morethan one location (e.g., stored in a plurality of servers incommunication with, for example, network 101).

As illustrated in FIG. 24, memory 2400 may store software instructionsto execute an information identification module 2401, a tendencyposition identification module 2402, an action execution module 2403, asystem user input module 2404, a database access module 2405, andlegislator database(s) 2406. Information identification module 2401 mayinclude software instructions for scraping the Internet to identifypending legislative bill(s) and information about legislators slated tovote on those bill(s). Tendency position identification module 2402 mayinclude software instructions for parsing the identified information todetermine a tendency position (e.g., whether the legislator will votefor or against a bill) for each legislator. Action execution module 2403may include software instructions to cause the occurrence of an action(e.g., display of a virtual whipboard) based on the determined tendencypositions. System user input module 2404 may include softwareinstructions for receiving a system user selection of a group oflegislators selected for a communication interaction (e.g., an email)and/or for receiving a system user selection of one or more legislativefunction categories (e.g., a person's position or job title). Databaseaccess module 2405 may include software instructions executable tointeract with legislator database(s) 2406, to store and/or retrieveinformation.

Information identification module 2401 may include software instructionsfor scraping the Internet to identify a currently pending legislativebill. For example, the software instructions may direct a processor toaccess publically available information from online websites associatedwith government entities, non-profit corporations, privateorganizations, etc. The scraped information might include policymakingdocuments, including legislative bills in the form of proposedlegislation, regulations, or judicial proceedings from variousgovernment bodies, including at the local, state, or federal level. Thecurrently pending legislative bill may include, for example, aregulation proposed by an administrative agency (e.g., a rulepromulgated by the United States Patent and Trademark Office), a federalbill proposed by a member of Congress (e.g., a health care bill), anongoing case in the Fifth Circuit Court of Appeals, or any other type oflegislative bill.

The data collection via Internet scraping may occur at any desired timeinterval. For example, the Internet may be scraped at preset periods(e.g., once per day), at initiated periods (e.g., in response to userinput directing such scraping), in real-time (i.e., continuouslythroughout a given operation), etc., as described above.

Information identification module 2401 may further include softwareinstructions for scraping the Internet to identify information aboutlegislators slated to vote on the identified pending legislative bill.For example, the information about legislators slated to vote on thebill may include, for one or more of the legislators, party affiliation,past voting history on similar or opposing bills, written or auditorypublic comments (e.g., speeches, opinion pieces in newsletters, etc.),features of the represented legislative district (e.g., which industriessupply jobs in the legislator's district), or any other availableinformation about characteristics, prior actions or tendencies, orproclivities of the legislator.

Tendency position identification module 2402 may include softwareinstructions for parsing or modeling the collected information todetermine a tendency position for each legislator. The tendency positionmay be an indicator of a likelihood that the legislator tends toward oneposition and/or away from an opposite position with respect to thelegislative bill. For example, the tendency position may be a percentagelikelihood that the legislator will vote for a proposition set forth inthe bill. For further example, the tendency position may be a percentagelikelihood that the legislator will not vote for the opposite of aproposition set forth in the bill. As such, the tendency position mayreflect a prediction of how each legislator is likely to vote on apending bill. Further, the tendency position may be expressed in termsof percentages, absolute numbers, fractions, or any other suitablenumerical or qualitative indicator of a likelihood that legislator willcast a vote in a given direction on the bill.

In some embodiments, the tendency position for a given legislator may bedetermined based on any combination of available information. Forexample, the tendency position may be determined based on one or morecampaign contributions. For instance, if an organization (e.g., PlannedParenthood) that is a proponent of a given bill (e.g., funding forwomen's health needs) contributed a large sum of money to thelegislator's election campaign, the tendency position for the legislatormay favor a “yes” vote on the pending bill.

In one embodiment, the tendency position may be determined based on oneor more prior votes of the legislator. For example, if the legislatorhas voted with a pro-gun inclination in the past, then the legislatormay tend toward other gun owner rights bills. In other embodiments, thetendency position may be based on any additional factors that provideinsight into how a legislator is likely to vote on a pending bill,including but not limited to party affiliation, public statements of thelegislator or his/her staff, etc. In other embodiments, the tendencyposition may be the outcome of a model, as described above, whose inputconsists of one or more factors described above.

Action execution module 2403 may be configured to perform a specificaction in response to the identified information. For example, actionexecution module 2303 may transmit a virtual whipboard for display to asystem user. The virtual whipboard may group legislators into aplurality of groups based on the tendency positions determined by thetendency position identification module 2402. As used herein, the term“virtual whipboard” refers to a virtual representation of the likelihoodthat one or more legislators are likely to vote for or against a givenbill. For example, in some embodiments, the outcomes and likelihoods ofeach legislator voting for a specific bill on the floor may beelectronically presented in a table (i.e., a whipboard) in whichlegislators are grouped together in several buckets representative ofthe likelihood of their voting. For instance, all legislators likely tovote “yes” (e.g., yea) on a bill may form a first group, all legislatorslikely to vote “no” (e.g., nay) on a given bill may form a second group,and all legislators that are a “toss up” as to the bill may form a thirdgroup.

System user input module 2404 may include software instructions forreceiving a system user selection of one or more of the plurality ofgroups displayed on the virtual whipboard. The system user may selectone or more groups for a communication interaction based on thedetermined tendency position of the group. As used herein, the term“communication interaction” refers to any way or manner of exchanginginformation or connecting with one or more other individuals orentities. For example, the communication interaction may be emailing,calling on the phone, broadcasting a message directly to the legislator,or to a set of individuals requesting that they communicate with thelegislator, sending a text message, etc.

In some embodiments, the virtual whipboard may be manipulated by thesystem user, for example, to filter by various attributes oflegislators, such as party affiliation, voting likelihood, length ofservice, etc. As a further example, proprietary data may be used by theuser of the system to filter legislators. Once manipulated in thismanner, the user may provide the system user input module 2404 with aselection of one or more of the groups based on the tendency position ofthe group. For example, the user may select the “toss up” group fortargeting with communications intended to sway members of the grouptoward a “yes” vote. The system user may provide his/her selection viaany variety of suitable means, depending on implementation-specificconsiderations. For example, the system user may interact with a userinterface displaying the virtual whipboard (e.g., by pressing a “select”button with his/her finger, or using a selector, such as a mouse, toselect the desired group). For further example, the system user mayselect the desired group by accessing a dropdown menu in standalonesoftware integrated with the virtual whipboard. In other embodiments,the user may provide a voice command that is recorded and/or translatedinto an action (e.g., determining a group selection indication) by theaction execution module 2403. Indeed, the system user may providehis/her selection via any suitable interface.

Database access module 2405 may access legislator database 2406 toretrieve legislative communication addresses of legislative personnelscraped from the Internet and divided into a plurality of legislativefunction categories. The legislative communications addresses may be anytype of address at which a person can be reached. For example, thelegislative communication addresses may be email addresses, physicalhome addresses, physical business addresses, phone numbers, websiteURL's including contact forms, social media accounts, etc. A legislativefunction category may be a person's position or job title. For example,the legislative function category may be chief of staff, deputy chief ofstaff, legislative assistant, congressional aide, legislativecorrespondent, legislator, etc.

System user input module 2404 may receive from the system user aselection of which legislative function categories are desired for thecommunication interaction. For example, the system user may want to swaya given legislator to vote “yes” on a bill. However, since it isunlikely the legislator will respond to a communication directly, thesystem user may target a member of the legislator's staff, such as thelegislator's chief of staff. Once the user has selected the legislativefunction categories and groups of legislators to target, actionexecution module 2403 exports the communication addresses of thelegislative personnel associated with the user's selections to acommunication platform. For example, the email addresses of the staffmembers of each of the legislators in the “toss up” group may beexported to the system user's email account to enable the system user tosend an email to the staff members without the need to manually inputeach of the addresses for each of the staff members. However, it shouldbe noted that exporting to the communication platform may include anysuitable form of exporting the addresses from the legislator database,such as interfacing with a module within the system, or interfacing witha standalone email system of the system user.

Legislator database 2406 may be configured to store any type oflegislator information of use to modules 2401-2405, depending onimplementation-specific considerations. For example, in embodiments inwhich the system user desires to target the legislators personally,legislator database 2406 may store publicly available information abouta given legislator. In other embodiments, legislator database 2406 maystore information associated with the system user's prior selections forprior legislative bills (e.g., if the system user typically selects“toss up” groups). Indeed, legislator database 2406 may be configured tostore any information associated with the functions of modules2401-2405.

FIG. 25A illustrates an example of a virtual whipboard 2501, and FIG.25B illustrates a communication 2502 that can be generated via use ofthe virtual whipboard 2501 by the system user. As illustrated, thevirtual whipboard 2501 may display a first panel 2503 for a first groupof legislators, a second panel 2504 for a second group of legislators, athird panel 2514 for a third group of legislators, and additional panels2505 for any additional groups of legislators. On each panel, agraphical indication 2506 for each legislator assigned to a given groupmay be displayed. The graphical indication 2506 may be any indicationthat allows the system user to identify the legislator. For example, thegraphical indication may be the legislator's name, picture, state,initials, chief of staff, etc. Further, each of panels 2503, 2504, 2514,and 2505 may include a select button 2508 that enables the system userto select that group for inclusion in the communication interaction.

As shown in FIG. 25B, communication 2502 is an email message includingcommunication addresses 2510 corresponding to the selected group(s) oflegislators and legislative function categories. In some embodiments,system user input module 2404 may further enable the system user toprovide a common message for export to the legislative personnelassociated with the selected group(s) of legislators. For example, thevirtual whipboard 2501 may provide an option for the system user todraft a message. The message may then be exported and displayed in themessage field 2512 of communication.

It should be noted that the illustrated virtual whipboard 2501 andcommunication 2502 are merely examples subject to a variety ofimplementation-specific variations. For example, the quantity of groupsdisplayed may vary. In one embodiment, three groups corresponding toyes, no, and toss-up likelihoods of voting on the bill may be provided.In another embodiment, at least five categories of groups may beprovided, including, for example, affirmative, leaning affirmative,neutral, leaning negative, and negative. Still further, in otherembodiments, additional degrees of likelihood of voting for or againstthe bill may be provided.

Further, in some embodiments, the virtual whipboard 2501 may display oneor more sorting options to the system user. For example, the virtualwhipboard 2501 may enable the user to sort the legislators by partyaffiliation, represented district or state, or any other identifyingcharacteristic. Still further, the virtual whipboard 2501 may enable thesystem user to message the legislative personnel associated with theselected group of legislators and legislative function categoriesdirectly from a messaging interface of the virtual whipboard 2501. Thatis, the virtual whipboard 2501 may display a “mailing list” option thatenable the user to generate an email directly in the virtual whipboard2501 interface.

FIG. 26 illustrates a method 2600 for using the virtual whipboard 2501in conjunction with a communication system in accordance with adisclosed embodiment. Method 2600 may, for example, be executed by oneor more processors of a server (e.g., central server 105 of FIG. 1) orany other appropriate hardware and/or software. Further, when executingmethod 2600, the one or more processors may execute instructions storedin any one of the modules discussed above.

In accordance with the method 2600, the server may identify alegislative bill and information about legislators slated to vote on thebill at block 2602. Block 2602 may be facilitated by softwareinstructions of information identification module 2401. Informationidentification module 2401 may be configured to scrape the Internet toidentify a currently pending legislative bill relevant to the systemuser. For example, if the system user is interested in bills influencingthe environment, the Internet may be scraped to identify bills beingdiscussed on environmental blogs, or mentioned on a governmental websiteassociated with the environment. Once the bill is identified,information identification module 2401 may identify information aboutthe legislators slated to vote on the bill, as described in detailabove.

The identified information about the legislators may be processed viasoftware steps executed by tendency position identification module 2402.For example, at block 2603, the server may execute tendency positionidentification module 2402 to determine a tendency position for eachlegislator slated to vote on the identified bill. For further example,tendency position identification module 2402 may determine whether eachlegislator takes a position that is affirmative, leaning affirmative,neutral, leaning negative, or negative.

At block 2604, the server may execute action execution module 2403 totransmit for display to a system user the virtual whipboard groupinglegislators into one or more groups based on the tendency positionsdetermined in block 2603. For example, the virtual whipboard 2501 may bedisplayed to the system user to show which legislators were assigned towhich groups. This display enables the system user to select one or moreof the displayed groups for a communication interaction, and the userselection is received by the system user input module at block 2605. Forexample, the system user may select to target the legislators grouped inthe leaning affirmative, neutral, and leaning negative groups in anattempt to sway the legislators from their current tendency positions.

At block 2606, the server may execute database access module 2405 toaccess legislator database 2406 to retrieve legislative communicationaddresses of legislative personnel scraped from the Internet and dividedinto a plurality of legislative function categories. At block 2607,system user may then provide a selection of one or more legislativefunction categories (e.g., legislative staff) for the communication 2502via system user input module 2404. The communication addressescorresponding to the system user's selections of the legislator group(s)and legislative function categories are then exported, for example, tothe system user's email system. In this way, the system user may targetcommunication 2502 to the people associated with the legislators in thetarget tendency position groups.

Altering Issue Outcomes

As described in more detail below, presently disclosed embodimentsinclude systems and methods for predicting and prescribing actions forimpacting policymaking outcomes. Policymaking is understood to includelegislation, rulemaking decisions, and the like. For example, in someembodiments, a prediction may be rendered with respect to whether agiven policymaking outcome (e.g., bill will pass, bill will not pass,regulation will be adopted, regulation will not be adopted, etc.) islikely to occur. Further, in one embodiment, the system may include aprescription engine that prescribes particular actions that can be takenin order to increases the chances of achieving a desired outcome. Forexample, the prescription engine may advise the system user to engage ina direct mailing campaign directed at a particular policymaker to swaythe policymaker to decide in accordance with the user's desired outcomefor the policy.

In some embodiments, the initial prediction of the outcome withrespected to the pending policy may include both a prediction of thevoting direction of each policymaker and a measure of the confidencelevel with which the voting prediction is made. For example, one initialprediction may be that a given policymaker is likely to decide in theaffirmative for the policy. The confidence level may then be set at 90%if, for example, the policymaker consistently decides for bills orregulations favorable to a cause furthered by the bill or regulation(e.g., environmental causes). In that way, the confidence level may bereflective of the policymaker's prior behavior. In other embodiments,however, the confidence level may be reflective of the confidence thesystem has in its analysis of the voting propensity of the policymaker.For example, if a greater amount of information is available about thepolicymaker's past decisions, the confidence level may be greater thanif the system is working with a sparser set of information about thepolicymaker.

FIG. 27 is a diagram illustrating an example of memory 2700 storing aplurality of modules. The modules may be executable by one or moreprocessors included in a server (e.g., central server 105, discussedabove). Further, in other embodiments, the components of memory 2700 maybe distributed over more than one location (e.g., stored in a pluralityof servers in communication with, for example, network 101).

As illustrated in FIG. 27, memory 2700 may store software instructionsto execute an information identification module 2701, an outcomeprediction module 2702, an outcome alteration module 2703, a databaseaccess module 2704, and database(s) 2705. Information identificationmodule 2701 may include software instructions for receiving event data,which may be scraped or proprietary data related to a policymakingprocess (e.g., information that a particular piece of legislation ispending) and alignment data associated with the event data (e.g.,information indicating whether legislators slated to vote on thelegislation are predicted to align with or against the legislation) fromat least one internet server. Outcome prediction module 2702 may includesoftware instructions for generating assumption data, a predictedoutcome and likelihood (e.g., an initial prediction and likelihood ofhow each legislator is likely to vote, and the initial prediction andlikelihood of the legislation being enacted). Outcome alteration module2703 may include software instructions for determining actions that maysuccessfully alter predicted outcome to a desired outcome (e.g.,increase the likelihood that a bill will be enacted). Database accessmodule 2704 may include software instructions executable to interactwith database(s) 2705, to store and/or retrieve information.

Information identification module 2701 may include software instructionsfor receiving event data and alignment data associated with the eventdata, which may be scraped data from at least one internet server. Eventdata may refer to information indicating that a given thing is going tooccur. For example, event data may be scraped data that includesinformation that a particular piece of legislation is pending. Forfurther example, scraped data may include information that anenvironmentally related bill has been proposed in Congress and is underconsideration.

Information identification module 2701 may further receive from a systemuser, via an interactive interface of a browser, a desired outcomeindicator corresponding to the event. A desired outcome indicator, asused herein, may be an indication of a desired result with respect tothe event data. For example, if the scraped data is the information thata policy is pending, the desired outcome indicator may be whether a userwould like the policy to pass or not pass, be adopted, not pass, or notbe adopted. If the user has not provided a desired outcome indicator fora given policy, a system-generated or user-generated model forpredicting the user position (e.g. desired outcome) may be used in placeof the explicit user position on a policy.

More particularly, in some embodiments, information identificationmodule 2701 may store software instructions for scraping the Internet toidentify a currently pending policy (e.g., a legislative bill) oraccessing information scraped from the Internet. For example, thesoftware instructions may direct a processor to access publicallyavailable information from online websites associated with governmententities, non-profit corporations, private organizations, etc. Thescraped information might include legislative bills in the form ofproposed legislation, regulations, or judicial proceedings at the local,state, or federal level. The currently pending legislative bill mayinclude, for example, a regulation proposed by an administrative agency(e.g., a rule promulgated by the United States Patent and TrademarkOffice), a federal bill proposed by a member of Congress (e.g., a healthcare bill), an ongoing case in the Fifth Circuit Court of Appeals, orany other type of legislative bill.

The data collection via Internet scraping may occur at any desired timeinterval. For example, the Internet may be scraped at preset periods(e.g., once per day), at initiated periods (e.g., in response to userinput directing such scraping), in real-time (i.e., continuouslythroughout a given operation), etc.

Information identification module 2701 may further include softwareinstructions for scraping the Internet, or accessing information scrapedfrom the Internet, to identify information about policymakers slated todecide on the identified pending policy. For example, the informationabout policymakers slated to decide on the policy may include, for oneor more of the policymakers, party affiliation, past voting history onsimilar or opposing policies, written or auditory public comments (e.g.,speeches, opinion pieces in newsletters, etc.), features of therepresented legislative district (e.g., which industries supply jobs inthe policymaker's district), or any other available information aboutcharacteristics, prior actions or tendencies, or proclivities of thepolicymaker.

Outcome prediction module 2702 may store software instructions forgenerating assumption data and/or applying one or more models asdiscussed above. Assumption data, as used herein, may represent anassumed value for a feature used by the model. For example, in oneembodiment, assumption data may indicate a likelihood of an affirmative(or negative) vote for each legislator of a plurality of legislatorsslated to vote on a piece of legislation. Assumption data may alsoindicate a likelihood of a particular piece of legislation passing ornot passing. The assumption data may be output to an interface in someembodiments.

Outcome prediction module 2702 may receive information from databaseaccess module 2704 and use the received information as input in one ormore models. After applying one or more models, prediction and outputprediction module 2702 may output one or more predictions and/or one ormore likelihoods related to the policy. Instead of using raw scrapeddata, in some embodiments, the server may extract one or more featuresfrom the data, as discussed above, to use as inputs for the model. Inother embodiments, the extracted features and their assumed values mayconstitute the assumption data. Still further, outcome prediction module2702 may calculate a success probability of the desired outcome based onthe assumption data and the desired outcome data. Success probability,as used herein, may be an indicator of a likelihood of success of theevent. For example, success probability may correspond to a probabilitythat a legislative bill will pass. Further, it should be noted thatsuccess probability may be expressed in any suitable manner, includingbut not limited to a percentage, a ratio, etc.

More specifically, in one embodiment, outcome prediction module 2702 mayinclude software instructions for parsing the scraped information todetermine a prediction relating to an outcome of the pending policy. Theinitial prediction (e.g., assumption data) may include a prediction foreach of a plurality of policymakers slated to decide on the pendingpolicy and a measure of the propensity of each of the plurality ofpolicymakers to decide in accordance with the initial prediction. Forexample, in one embodiment, the initial prediction may indicate that agiven policymaker is going to decide affirmatively for the pendingpolicy with a confidence level of 90%. Once determined, outcomeprediction module 2702 may display to the system user the initialprediction. The initial prediction may be displayed in any suitablemanner. For example, the initial prediction may display a symbolcorresponding to a graphical representation of an affirmative ornegative position, along with a percentage confidence of the position.

Outcome alteration module 2703 may include software instructions forgenerating a suggested alteration to the assumption data that impactsthe success probability. In some embodiments, the suggested alterationmay be generated based on the scraped data, proprietary data, and thesuccess probability. For example, in one embodiment, the suggestedalteration to the assumption data may be a change as to whichpolicymakers (e.g., legislators) are likely to decide (e.g., vote) inthe affirmative (or negative) for the pending policy (e.g., alegislative bill).

More specifically, in some embodiments, outcome alteration module 2703may access second information to identify an action likely to change atleast one of the assumption data. For example, second information couldidentify an action likely to change at least one of the initialprediction and the propensity of at least one policymaker to generate asubsequent prediction corresponding to an increase in a likelihood ofachieving the desired outcome. For example, outcome alteration module2703 may access information scraped from the Internet that indicatesthat when a given policymaker receives a large number of letters fromhis/her constituents in support of the policy, he/she typically decidesfor the policy. Outcome alteration module 2703 may then display to thesystem user a recommendation to take the action in order to increase thelikelihood of achieving the desired outcome. For example, the systemuser may receive a prompt indicating the user should motivate thepolicymaker's constituents to send letters to the policymaker in supportof the policy.

However, it should be noted that the provided recommendation may be anysuitable action, depending on implementation-specific considerations.For example, the recommendation may be to take action for regulatorypolicymaking. In such embodiments, the recommendation may includeidentifying language to change the pending regulation, identifyinglanguage to be added to the pending regulation, identifyingpolicymakers, and/or suggesting at least one organization that desiresthe desired outcome or an outcome related to the desired outcome. Forfurther example, in other embodiments, the recommendation may be to takeaction for judicial policymaking. In such embodiments, therecommendation may include identifying language to present in briefs,identifying language to be used in oral arguments, identifyingpolicymakers, and/or suggesting at least one organization that desiresthe desired outcome or an outcome related to the desired outcome.

As discussed above, FIG. 19E illustrates an embodiment of a graphicaluser interface that may be used for viewing information related toinfluencing an outcome. The server may generate an initial prediction ofthe user(s)′ likelihood of supporting the bill. In some embodiments,based on that predicted desired outcome indicator, the outcomealteration module 2703 may recommend two sets of actions to alter theassumption data. The first set of assumption data may relate to thecontribution a cosponsor makes to the success of a bill. For example, ifthe bill has no cosponsors, the predicted outcome may have nocontribution from cosponsors, but the assumption data prediction mayindicate that by convincing either Sen. Rob Smith or Sen. Kevin Bloom,for example, to cosponsor, the success probability will increase. Thesecond set of assumption data may relate to the voting likelihood ofindividual legislators. The assumption data may suggest that Sen. JulieAndrews is currently 37% likely to vote affirmatively, but by contactingher and motivating her to vote yes, she would increase the successprobability by 13%.

Database access module 2704 may access database(s) 2705 to retrieveinformation used by modules 2701-2703. Database(s) 2705 may beconfigured to store any type of information of use to modules 2701-2703,depending on implementation-specific considerations. For example,database(s) 2705 may store the first information (e.g., informationscraped from the Internet about a plurality of policymakers) and/or thesecond information (e.g., information regarding what public actionsinfluenced a policymaker in the past). Indeed, database(s) 2705 may beconfigured to store any information associated with the functions ofmodules 2701-2704.

FIG. 28 illustrates an embodiment of a policymaking outcome predictiondisplay 2800. As illustrated, display 2800 may be configured toillustrate to a system user the initial and subsequent predictions withrespect to a given event. For example, in the illustrated embodiment,display 2800 includes a first panel 2801, a second panel 2802, and anN^(th) panel 2803, each corresponding to a policymaker of a plurality ofpolicymakers slated to decide on a policy. Each panel 2801-2803 mayinclude an initial prediction and a subsequent prediction. For example,panel 2801 includes a display of a policymaker block 2805 configured todisplay an indication as to which policymaker the predictions areassociated with. Policymaker block 2805 may be a graphical display of apolicymaker's name, picture, represented district, etc.

An initial prediction block 2806 may include a graphical indication ofan initial prediction for the first policymaker. For example, initialprediction block 2806 may indicate that the first policymaker is likelyto decide in the affirmative for a given policy. A tendency block 2807may indicate a confidence level for the initial prediction in block2806. For example, tendency block 2807 may indicate that the firstpolicymaker is 75% likely to decide in the affirmative. Further, asubsequent prediction block 2808 may be configured to indicate anupdated prediction associated with the user taking a particular action(e.g., engaging in a direct mailing campaign targeting the firstpolicymaker).

The illustrated display 2800 also includes a recommendation panel 2804including a recommendation block 2810 configured to display arecommendation to the user. For example, recommendation block 2810 mayadvise the user to engage in a direct mailing campaign targeted at thepolicymaker, call the policymaker's office, form a protest outside thepolicymaker's office, etc. Recommendation panel 2804 may display thesystem recommendation in any suitable manner. For example, it maydisplay words that can be read by the user, graphics illustrating therecommended action, a button to click that enables an audio recording ofthe recommendation to play, etc. Indeed, recommendation panel 2804 maydisplay the recommendation in any suitable manner.

The illustrated display 2800 also includes a recommendation panel 2804including a recommendation block 2810 configured to display arecommendation to the user. For example, recommendation block 2810 mayadvise the user to engage in a direct mailing campaign targeted at thepolicymaker, call the policymaker's office, form a protest outside thelegislator's office, etc. Recommendation panel 2804 may display thesystem recommendation in any suitable manner. For example, it maydisplay words that can be read by the user, graphics illustrating therecommended action, a button to click that enables an audio recording ofthe recommendation to play, etc. Indeed, recommendation panel 2804 maydisplay the recommendation in any suitable manner.

FIG. 29 illustrates a method 2900 for generating a suggested alterationto the assumption data to impact the success probability of an event inaccordance with a disclosed embodiment. Method 2900 may, for example, beexecuted by one or more processors of a server (e.g., central server 105of FIG. 1) or any other appropriate hardware and/or software. Further,when executing method 2900, the one or more processors may executeinstructions stored in any one of the modules discussed above.

In accordance with method 2900, the server may receive event data atblock 2901. Block 2901 may be facilitated by software instructions ofinformation identification module 2701. Information identificationmodule 2701 may be configured to access scraped data from database 2705via database access module 2704. For example, information identificationmodule 2701 may access the database 2705 to identify informationindicating that a particular policy is pending.

At block 2902, the server may receive alignment data including supportor opposition data of at least one individual relating to the outcome ofan event. For example, the alignment data may indicate that at least onelegislator supports or opposes pending legislation. The server mayfurther receive or compute a desired outcome indicator corresponding tothe event at block 2903. The desired outcome indicator may be receivedfrom a system user via an interactive interface of a browser in someembodiments. For example, the desired outcome indicator may be thesystem user's desire for the pending bill to pass or not pass.

At block 2904, the server may be configured to generate assumption data.The assumption data may be generated based on the scraped proprietarydata in some embodiments. For example, the assumption data may includeinformation indicating the likelihood that each of the plurality ofpolicymakers slated to decide on the pending policy will support oroppose the policy. Further, outcome prediction module 2702 may beconfigured to calculate a success probability of the desired outcome atblock 2905. For example, the success probability may be the probabilitythat the pending policy will pass or be adopted.

At block 2906, the server may be configured to generate a suggestedalteration to the assumption data that would further the system user'sdesired outcome with respect to the event. For example, the suggestedalteration may include a suggestion to target a given policymaker withdirect mailings, phone calls, emails, etc.

FIG. 30 illustrates a method 3000 for generating an actionrecommendation to a user in accordance with a disclosed embodiment.Method 3000 may be carried out, for example, by one or more processorsincluded in a server (e.g., central server 105, discussed above) or anyother appropriate hardware and/or software. Further, when executingmethod 3000, the one or more processors may execute instructions storedin any one of the modules discussed above.

In accordance with method 3000, the server may identify pending policyand information about policymakers slated to decide on the policy atblock 3001. For example, information identification module 2701 mayaccess database 2705 to retrieve information scraped from the Internetabout a plurality of policymakers slated to decide on a given policy. Insome embodiments, the information about policymakers slated to decide onthe policy may include, for one or more of the policymakers, partyaffiliation, past voting history on similar or opposing policies,written or auditory public comments (e.g., speeches, opinion pieces innewsletters, etc.), features of the represented legislative district(e.g., which industries supply jobs in the policymaker's district), orany other available information about characteristics, prior actions ortendencies, or proclivities of the policymaker.

At block 3002, the identified information about the policymakers may beused to generate an initial prediction relating to an outcome of thepending policy. For example, the initial prediction may include ainitial prediction for each policymaker, which indicates whether thepolicymaker is likely to decide in the affirmative or negative for thepending policy. The initial prediction may further include a measure ofthe propensity of each of the plurality of policymakers to decide inaccordance with the initial prediction. For example, the measure of thepropensity may be a confidence level assigned to each initialprediction.

At block 3003, the server may display the initial prediction to theuser, for example, as shown on display 2800. At block 3004, the servermay then identify an action likely to change the initial prediction. Forexample, if a policymaker has been swayed by letters from his/herconstituents in the past, this action may be sufficient to change thepolicymaker from a negative prediction to a positive prediction for thepending policy.

At block 3005, the server is configured to generate a subsequentprediction. For example, outcome alteration module 2703 may beconfigured to indicate an updated prediction associated with the usertaking a particular action (e.g., engaging in a direct mailing campaigntargeting the first policymaker). The server may then display an actionrecommendation to the user at block 3006. For example, outcomealteration module 2703 may recommend that the user engage in an action(e.g., targeted mailing to a policymaker) consistent with the user'sdesired outcome for the pending policy.

Correlating Comments and Sentiment to Policy Document Sub-Sections

In some embodiments, the policymaking being analyzed may consist ofmulti-sectioned documents. For example, regulatory documents associatedwith a rule (or regulation) proposed in the rule-making process havemultiple sections. These policies may be associated with a variety ofpotential outcomes. For example, the set of potential outcomes mayinclude the likelihood of rule promulgation (i.e., the likelihood thatthe rule will be adopted), information related to the timeline of arule-making process (i.e., the estimated amount of time until the ruleis voted on, adopted, or denied), arguments made for or against therule, likelihood of regulatory enforcement, the form in which theregulatory document will be finalized (i.e., what language may beincluded in the rule), the impact of the rule (including favorabilityand significance), and the factors helping or hurting the likelihood ofany aforementioned potential outcome. The factors helping or hurting thelikelihood may include, for example, policymakers or events.

Multi-sectioned documents may have different predicted outcomes for eachsection. For example, a bill is a multi-sectioned document, where afirst section of a bill may be enacted, while a second section may beremoved prior to enactment. The outcome of the policymaking, and theoutcome of each section of the multi-sectioned policy document can beinfluenced by comments.

In some embodiments, comments may include those that are consideredofficially submitted comments, while other comments may not beofficially submitted. For example, officially submitted comments mayinclude statements of position, arguments, transcripts, scientificstudies, meeting notes, financial analyses, and the like, which are sentto the policymakers directly. By contrast, a comment may also includedocuments not officially submitted to the policymakers, such as publicstatements from individuals, organizations, companies, social media, andnews. As used throughout the present disclosure, a “comment” may referto any of the above described comments (both officially submitted andnot). For example, the potential outcomes of a rule may be greatlyinfluenced by reactions and feedback throughout the rule-making process.One of the primary forms of feedback is through the notice and commentprocess. Individuals and organizations, including private and publiccompanies, government bodies, and foreign entities may submit theirpositions regarding the proposed rule. Regulatory agencies then reviewand respond to these comments made during the process prior to issuing afinal rule (or obtaining additional feedback). Analysis of the submittedcomments (which can often be extensive and, for example, exceed over amillion comments per rule) may increase an understanding as to what theoutcome of the final rule will be.

FIG. 31 is a memory 3100 consistent with the embodiments disclosedherein. In some embodiments, memory 3100 may be included in, forexample, central server 105, discussed above. Further, in otherembodiments, the components of memory 3100 may be distributed over morethan one location (e.g., stored in a plurality of servers incommunication with, for example, network 101).

Memory 3100 may include a database 3101, which may store informationabout multi-sectioned documents and comments. Memory 3100 may alsoinclude database access module 3103, which may be used to accessinformation stored in database 3101 about a multi-sectioned document orcomment. In some embodiments, the multi-sectioned documents stored indatabase 3101 may include one or more proposed policies. In someembodiments, the multi-sectioned documents stored in database 3101 mayinclude one or more proposed bills. In some embodiments, the commentsstored in database 3101 may be public comments on the one or moreproposed policies. The information stored in database 3101 may begathered through a variety of sources, for example, one or more ofsources 103 a-103 c, as shown in FIG. 1 via, for example, network 101(e.g., the internet).

Internet scraping module 3105 may gather information from open internetresources, such as legislative websites, regulatory websites, newswebsites, financial websites, and social media websites, or other datarepositories provided by the user, such as proprietary datarepositories, contained in repositories, including those not hosted onthe internet (e.g., repositories available on internal networks, useraccessed networks, and the like), which may be stored in database 3101,as described above. In general, internet scraping module 3105 may gatherinformation about multi-sectioned documents and comments, which isstored in database 3101.

In some embodiments, the comments stored in database 3101 may beassociated with certain sections of a multi-sectioned document usingassociation analysis module 3107. For example, sections of themulti-sectioned document may be explicitly indicated in the comment(through citation, linking, and the like), or they may be implicit.Implicit mentions of the multi-sectioned document sections in thecomment may be mapped from the comment to the multi-sectioned documentthrough a mapping mechanism of association analysis module 3107.

In some embodiments, one such mapping mechanism may be in the form of amodel used by association analysis module 3107. A model may representeach subsection of a comment as a feature vector, each subsection of amulti-sectioned document as a feature vector, and compute the similaritybetween feature vectors to map subsections of the comment to subsectionsof the multi-sectioned document. Many methods of computing similarity ofvectors are available, including cosine similarity, kernel functions, orEuclidean distance. For example, nearest-neighbor search may be used fordetection of a similar section between the comment and multi-sectioneddocument. Furthermore, a clustering model may be used, whichautomatically groups similar vectors together (either in a flat orhierarchical fashion to a specified depth).

In some embodiments, a text analysis module 3109 may determine thesentiment of a comment stored in database 3101. The sentiment mayinclude a stance, position, or an argument, or general dispositiontoward the multi-sectioned policy document, or the like, with varyingdegrees of stance, position, of disposition. In some embodiments, thesentiment of a comment may express support for one section andopposition towards a different section of the multi-sectioned document(or may express neither support nor opposition). In other embodiments,the sentiment of a comment may be either positive, negative, or neither.In some embodiments, there can be more than one sentiment associatedwith each comment. In yet further embodiments, the text analysis module3109 may also determine the levels of influence of each comment (e.g.,based on the author of the comment), and weigh each comment based on aninfluence level. For example, different commenters may be given a higheror lower influence level, which may then be applied to that author'scomment. As a further example, a comment authored by Morgan Stanley maybe deemed to have a higher influence on a proposed SEC rule than acomment from a local grocer. In some embodiments, a text analyticsfilter may be applied to the text data.

FIG. 32 is a depiction of an exemplary auto-correlation systemconsistent with one or more disclosed embodiments of the presentdisclosure. As depicted in FIG. 32, system 3200 may comprise a network3201, a plurality of comments, e.g., comments 3203 a, 3203 b, and 3203c, a central server 3205, modules 3207, and at least one multi-sectioneddocument 3209 containing a plurality of sections, e.g., sections 3211 a,3211 b, and 3211 c. One skilled in the art may vary the structure and/orcomponents of system 3200. For example, system 3200 may includeadditional servers—for example, central server 3205 may comprisemultiple servers and/or one or more sources may be stored on a server.By way of further example, one or more comments may be distributed overa plurality or servers, and/or one or more comments may be stored on thesame server.

Network 3201 may be any type of network that provides communication(s)and/or facilitates the exchange of information between two or morenodes/terminals and may correspond to network 101, discussed above. Forexample, network 3201 may comprise the Internet, a Local Area Network(LAN), or other suitable telecommunications network, as discussed abovein connection with FIG. 1. In some embodiments, one or more nodes ofsystem 3200 may communication with one or more additional nodes via adedicated communications medium.

Central server 3205 may comprise a single server or a plurality ofservers. In some embodiments, the plurality of servers may be connectedto form one or more server racks, e.g., as depicted in FIG. 2. In someembodiments, central server 3205 may store instructions to perform oneor more operations of the disclosed embodiments in one or more memorydevices. In some embodiments, central server 3205 may further compriseone or more processors (e.g., CPUs, GPUs) for performing storedinstructions.

In some embodiments, comments 3203 a, 3203 b, and 3203 c may be gatheredusing internet scraping module 3105. For example, comments 3203 a, 3203b, and 3203 c may be gathered from open internet resources, such aslegislative websites, regulatory websites, news websites, financialwebsites, and social media websites, or other data repositories providedby the user, such as proprietary data repositories, including those nothosted on the internet (e.g., repositories available on internalnetworks, user accessed networks, and the like). In some embodiments,central server 3205 may receive information about one or more comments,e.g., comments 3203 a, 3203 b, and 3203 c over network 3201.

In some embodiments, additional analysis may be performed on thecomments by the central server 3205 using modules 3207 (e.g., themodules 3207 may be the previously described association analysis module3107 and/or text analysis module 3109). As a result of this analysis, amulti-sectioned document 3209 may be analyzed by the system. Forexample, central server 3205 may use text analysis module 3109 todetermine the influence level of the comment, and apply a correspondingweight to each comment 3203 a, 3203 b, and 3203 c.

In some embodiments, the processor located in central server 3205 may beconfigured to predict, based on the weighted comments, a predictedoutcome for the entire multi-sectioned document or a section of it. Forexample, the predicted outcome may be whether a section of themulti-sectioned document will be revised prior to adoption. For example,a section of the multi-sectioned document 3209 that has many weightedcomments associated with it may provide the system with information forpredicting that the section may be revised prior to adoption. In otherembodiments, the processor located in central server 3205 may beconfigured to predict, based on the weighted comments, which sections ofthe multi-section document 3209 may change. In yet other embodiments,the processor located in central server 3205 may be configured topredict, based on the weighted comments, how likely an agency willchange at least one section of the multi-sectioned document 3209.

In some embodiments, the processor located in central server 3205 may beconfigured to determine that, based on the weighted comments, one ormore changes will be made to at least one section of the multi-sectioneddocument 3209. In other embodiments, the processor located in centralserver 3205 may be configured to recommend, based on the weightedcomments, changes in language to a section of the multi-section document3209 in order to increase a likelihood of adoption.

In some exemplary embodiments, the sections 3211 a, 3211 b, and 3211 cof the multi-sectioned document 3209 may include officially delineatedparts of the document, syntactically defined units of text (e.g., asentence, paragraph, page, or multiple pages), subjectively definedunits of text (e.g., a chapter), where the subjectively defined units oftext may share a property (e.g., relating to the same subject area orperforming the same action).

For example, within multi-sectioned document 3209, section 3211 a may bein the introduction part of the document, section 3211 b may be theargument part of the document, and section 3211 c may be the conclusionof the document. In other embodiments, within multi-sectioned document3209, sections 3211 a, 3211 b, and 3211 c may each be a single page ormultiple pages.

FIG. 33 is an example of a multi-sectioned document with correlatedcomments. For example, FIG. 33 may represent a visualization 3300, whichmay be displayed to a user on a user interface of a device (e.g., adisplay screen, a laptop, a handheld device such as a smartphone, ortablet, or a smartwatch). In some embodiments, the visualization 3300may include a graphical depiction of sentiment relative to the one ormore sections of the multi-sectioned document 3301. As shown in FIG. 33,a multi-sectioned document 3301 may have a plurality of sections, e.g.,sections 3303 a, 3303 b, and 3303 c. The visualization 3300 may alsoinclude a first comment 3305, and a second comment 3307. As shown inFIG. 33, the first comment 3305 may be auto-correlated to only onesection 3303 a of the multi-sectioned document 3301 using associationanalysis module 3107 and/or text analysis module 3109 as describedabove. Second comment 3307 may be associated with additional sections ofmulti-sectioned document 3301, i.e., sections 3303 a, 3303 b, and 3303c. In some embodiments, the visualization 3300 may include extractedtext from the multi-sectioned document 3301 juxtaposed with textualrepresentations of sentiment found in a comment 3305 or 3307. Asdescribed above, the sentiment of a comment 3305 or 3307 may expresssupport for one section (i.e., 3303 a, 3303 b, and 3303 c) andopposition towards a different section of the multi-sectioned document3301.

FIG. 34 is a depiction of an exemplary multi-sectioned document withcorrelated comments. For example, visualization 3400 may include amulti-sectioned document 3401, which may be displayed to a user on auser interface of a device (e.g., a display screen, a laptop, a handhelddevice such as a smartphone, or tablet, or a smartwatch). In someembodiments, the visualization 3300 may include a graphical depiction ofsentiment relative to the one or more sections of the multi-sectioneddocument 3401. For example, a set of comments 3403 may be associatedwith a highlighted portion of multi-sectioned document 3401. In someembodiments, the system user may interact with a highlighted portion ofmulti-sectioned document 3401 to display additional information about aset of comments 3403, including the published date, organization,submitter, sentiment analysis, and attachments associated with thecomments. In yet further embodiments, heatmap 3405 may highlight eachsection of multi-sectioned document 3401 in which positive, negative,supporting, opposing, or neutral language appears. The heatmap 3405 maydisplay various colors associated with a section of multi-sectioneddocument 3401 to indicate the various language being used.

FIG. 35 is an example of an auto-correlation method 3500, consistentwith the disclosed embodiments. Method 3500 may, for example, beexecuted by one or more processors of a server (e.g., central server 105of FIG. 1) or any other appropriate hardware and/or software. Further,when executing method 3500, the one or more processors may executeinstructions stored in any one of the modules discussed above.

In step 3501, the server may scrape the internet for text dataassociated with comments expressed by a plurality of individuals about acommon multi-sectioned document, the comments not being linked to aparticular section of the multi-sectioned document. For example,comments may be gathered from open internet resources, such aslegislative websites, regulatory websites, news websites, financialwebsites, and social media websites, or other data repositories, such asproprietary data repositories, including those not hosted on theinternet (e.g., repositories available on internal networks, useraccessed networks, and the like).

In step 3503, the server may analyze the text data in order to determinea sentiment associated with each comment. The sentiment may include astance, position, or an argument, or the like, as described above. Insome embodiments, the sentiment of a comment may express support for onesection and opposition towards a different section of themulti-sectioned document (or may express neither support noropposition). In other embodiments, the sentiment of a comment may beeither positive, negative, or neither. In yet further embodiments, thetext analysis module 3109 may also determine the levels of influence ofeach comment (e.g., based on the author of the comment), and weigh eachcomment based on an influence level. For example, different commentersmay be given a higher or lower influence level, which may then beapplied to that author's comment.

In step 3505, the server may apply an association analysis filter to thetext data in order to correlate at least a portion of each comment withone or more sections of the multi-sectioned document. For example,sections of the multi-sectioned document may be explicitly indicated inthe comment (through citation, linking, and the like), or they may beimplicit. Implicit mentions of the multi-sectioned document sections inthe comment may be mapped from the comment to the multi-sectioneddocument through a mapping mechanism of association analysis module3107.

In step 3507, the server may transmit for display to a system user avisualization of the sentiment mapped to one or more sections of themulti-sectioned document, as shown in FIGS. 33 and 34.

While the disclosure above has focused on primarily on predicting theoutcomes for comments made in relation to a regulation, and associatingcomments and stances to rules, and subsections of rules, these analysesmay also be performed in other policymaking areas, such as legislation.For example, comments made in support or opposition of a piece oflegislation may be associated with specific sections of the legislation,the stance and arguments of those comments may be computed, and aresulting prediction made to the likelihood of a section of thelegislation changing or being enacted.

Predicting Policy Adoption

Once a policy (e.g., a rule) is promulgated, a set of potential outcomesmay include the likelihood of policy promulgation (e.g., the likelihoodthat the rule will be adopted), information related to the timeline ofthe policymaking process (e.g., the estimated amount of time until arule is voted on, adopted, or denied), arguments made for or against thepolicy, likelihood of enforcement (e.g., regulatory enforcement), theform in which the document will be finalized (e.g., what language may beincluded in the rule), the impact of the policy (including favorabilityand significance), and the factors helping or hurting the likelihood ofany aforementioned potential outcome, where these factors helping orhurting the likelihood may include people or events. In someembodiments, the same may be determined regarding a legislative bill.

As previously discussed, in some embodiments, analysis of comments,which may often number in the millions of comments per rule, may beuseful for understanding what the outcome of the final rule may be.

In some embodiments, this set of potential outcomes may be predictedusing a model to generate a predicted outcome. Thus, in someembodiments, predicted outcomes of a policy may be generated byanalyzing the comments associated with a policy. In other embodiments,the predicted outcomes of a bill may be generated by analyzing thecomments associated with a bill. Furthermore, the arguments from thecomments may be aggregated to predict how the policy may change. Forexample, if one or more comments on a given policy suggest the policylacks clarity, is based on bad science, or is an overreach, or the like,a predicted outcome for that policy may be that it will include modifiedlanguage (e.g., additional clarifying language, additional scientificreporting, additional justifications for authority), or removal oflanguage.

In some embodiments, various data may be used within the modelconstructed for predicting if a policy will take effect, when a policywill take effect, what language will be modified and retained in thefinal version, how likely it is to be challenged, or how likely is it tobe enforced. For example, the data used may be any combination of thefollowing: the previous timelines of policies promulgated by theauthoring agency, the number of currently considered policies, the textof the policy itself, the statute or act the policy is drawing itsauthority from, the number of comments, the arguments determined fromcomments, the authoring organizations of comments, similar policies fromother agencies, other unrelated data as discussed above, and the like.

The comments may be associated with portions (e.g., sections) of thepolicy, allowing analysis to be applied to each subsection of thedocument. For instance, if one or more comments against the policy andarguing for clarity may be associated with a particular section of thepolicy, a predicted outcome for the policy may be that the associatedsection will be clarified. In another embodiment, the predicted outcomeof a policy may be how likely a particular section of a policy is to bechallenged, or how likely a policy and/or sections of a policy are to beenforced.

In some embodiments, the server may determine the predicted outcomes ofa policy and their associated likelihood based on model generated fromthe scraped data. The scraped data used to create this model may rely onthe comments themselves, related documents, and analysis thereof,including the content (e.g., some or all of the text data, terms, andother derived linguistic analysis consistent with the disclosure above)or metadata (e.g., dates of activity, author of comment, authorsorganization, and the like). For example, the server may apply one ormore models with some or all of the scraped data as one or more inputs.Instead of using raw scraped data, the server may extract one or morefeatures from the data, as discussed above, to use as inputs for themodel.

In some embodiments, this automated model and analysis may be carriedout in various ways. As described above, a document or policy may berepresented as a feature vector, and a function may be created whichproduces an association between the feature vector and the outcome. Thisfunction may be used to compute a score, which may be a likelihoodscore, or a probability, reflecting how likely a given document orpolicy is to result in certain predicted outcomes. For a comment, thisscore relays the likelihood of predicted outcomes (e.g., who and wherethe comment is from, does the author of the comment support the policy,the gravitas of the commenter, what arguments are being made, and thelike).

In some embodiments, the comment may be represented by the identity ofthe author. Specifically, the feature vector representing the commentmay only include the identity of the comment's author, without anyfurther analysis of the document. In some embodiments, the scoringmechanism of the function to compute a likelihood score for a givencomment document supporting the policy may then be based on a count ofhow many comments this author has submitted in the past that have beenopposed to policies, or may be further divided by regulation on thistopic, or from an agency.

The comment may also be represented by a feature vector comprised ofsome or all of the text, including terms and other derived linguisticanalysis and metadata (e.g., dates of activity, the individual ororganizational author, whether the comment is a commonly-used templateor unique, and the like) associated with the comment document.Linguistic analysis used may include sentiment dictionaries, parsers fordetecting various syntactic patterns, and other knowledge bases. In someembodiments, the features derived from analysis of the document may becombined with features derived from analysis of the author, forming alarger feature vector. In some embodiments, the analysis methodsdescribed herein used to generate the feature vector may use machinelearning techniques.

Author related characteristics may include whether the author is anindividual or an organization, where organization may be further dividedinto public, private, large, small, or a number of other designations.Authors may have further associated features, such as geography,previous activity, such as submitted comments, relationships toregulators, financial contributions, campaign financials, previousregulations supported, collaboration with other organizations orpolicymakers, public statements, news, and external information based ononline social presence.

FIG. 36 is a memory 3600 consistent with the embodiments disclosedherein. In some embodiments, memory 3600 may be included in, forexample, central server 105, discussed above. Further, in otherembodiments, the components of memory 3600 may be distributed over morethan one location (e.g., stored in a plurality of servers incommunication with, for example, network 101).

Memory 3600 may include a database 3601, which may store informationabout the proposed policy and comments. Memory 3600 may also includedatabase access module 3603, which may be used to access informationstored in database 3601 about a proposed policy or comment. In someembodiments, the comments stored in database 3601 may be public commentson the one or more proposed policies. The information stored in database3601 may be gathered through a variety of sources, for example, one ormore of sources 103 a-103 c, as shown in FIG. 1 via, for example,network 101 (e.g., the internet).

In some embodiments, a text analysis module 3605 may determine thesentiment of a comment stored in database 3601. The sentiment mayinclude a stance, position, argument, or the like, as described above.In some embodiments, the sentiment of a comment may express support forone or more sections and opposition towards a different section of thepolicy (or may express neither support nor opposition). In otherembodiments, the sentiment of a comment may be either positive,negative, or neither. A potential outcome of text analytics filtermodule 3605 may be the stance that the comments author is takingregarding the proposed policy.

In some embodiments, the text analysis module 3605 may be applied to thetext data in order to determine a sentiment, including matching at leastone piece of text data from the comment to at least one other piece oftext data stored in a database. For example, text analysis module 3605may associate each comment with a predicted stance (e.g., where a stancemay be supporting, opposing, or neutral), and the average stance may betaken as the predicted outcome of the policy, where if the averagestance is negative, the predicted outcome is for the policy to not beadopted in the current form, and if the current stance is positive, thepredicted outcome is for the policy to be adopted. The function tocompute stance can be represented by a model derived frommachine-learned algorithms or other computer-implemented policies, themodel comprised of policies, weights, and the like, in accordance withother models described in the present disclosure.

In some embodiments, the comments stored in database 3601 may have theirinfluence determined using influence filter module 3607. For example,influence filter module 3607 may also determine the levels of influenceof each comment (e.g., based on the author of the comment), and weigheach comment based on an influence level. For example, differentcommenters may be given a higher or lower influence level, which maythen be applied to that author's comment.

In some embodiments, the influence filter module 3607 may access adatabase of terms associated with a heightened degree of influence. Forexample, these terms associated with a heightened degree of influencemay include zip codes, organizations names, or addresses. In someembodiments, the system may automatically assign a heightened degree ofinfluence to specific terms, or alternatively, a user may input termswith which to assign a heightened degree of influence.

In some embodiments, an indicator determination module 3609 may generatean indicator associated with a proposed policy. In some embodiments, theindicator determined by indicator determination module 3609 is alikelihood measure reflecting a probability that the regulation will beadopted.

FIG. 37 is an example of a text analytics system consistent with one ormore disclosed embodiments of the present disclosure. As depicted inFIG. 37, system 3700 may comprise a network 3701, a plurality ofcomments, e.g., comments 3703 a, 3703 b, and 3703 c, a central server3705, comments weighted with high influence 3707, comments weighted withaverage influence 3711, and comments weighted with low influence 3715.One skilled in the art may vary the structure and/or components ofsystem 3700. For example, system 3700 may include additional servers—forexample, central server 3705 may comprise multiple servers and/or one ormore sources may be stored on a server. By way of further example, oneor more comments may be distributed over a plurality or servers, and/orone or more comments may be stored on the same server.

Network 3701 may be any type of network that provides communication(s)and/or facilitates the exchange of information between two or morenodes/terminals and may correspond to network 101, discussed above. Forexample, network 3701 may comprise the Internet, a Local Area Network(LAN), or other suitable telecommunications network, as discussed abovein connection with FIG. 1. In some embodiments, one or more nodes ofsystem 3700 may communication with one or more additional nodes via adedicated communications medium.

Central server 3705 may comprise a single server or a plurality ofservers. In some embodiments, the plurality of servers may be connectedto form one or more server racks, e.g., as depicted in FIG. 2. In someembodiments, central server 3705 may store instructions to perform oneor more operations of the disclosed embodiments in one or more memorydevices. In some embodiments, central server 3705 may further compriseone or more processors (e.g., CPUs, GPUs) for performing storedinstructions.

In some embodiments, the processor located in server 3705 may beconfigured to apply an association analysis filter to the text data inorder to correlate at least a portion of each comment with a particularsection of the proposed regulation 3709.

In some embodiments, comments 3703 a, 3703 b, and 3703 c may be storedin database 3601. In some embodiments, central server 3705 may receiveinformation about one or more comments, e.g., comments 3703 a, 3703 b,and 3703 c, over network 3701. Although discussed in connection withcentral server 3705, the preceding disclosure is exemplary in nature,and the execution of the method may be distributed across multipledevices (e.g., servers).

In some embodiments, the central server 3705 may weigh the comments 3703a, 3703 b, and 3703 c, into a plurality of categories. For example,central server 3705 may use text analysis module 3605 and influencefilter module 3607 to weigh comments, consistent with the disclosureabove. After weighing the comments, central server 3705 may categorizethe comments into one or more categories. As shown in FIG. 37, commentsmay be categorized into comments weighted with high influence 3707(e.g., comments 3709 a, 3709 b), comments weighted with averageinfluence 3711 (e.g., comments 3713 a, 3713 b), and comments weightedwith low influence 3715 (e.g., comments 3717 a, 3717 b). More commentcategories can be included by central server 3705 as desired.

FIG. 38 is a depiction of an example of a prediction with an indicatorassociated with adoption of the regulation. For example, sentimentindicator 3801 may depict the sentiment associated with a particularcomment, comment theme indicator 3803 may depict various argumentsassociated with the comments, and stance detection indicator 3805 maydepict graphically various stances taken within the comments. Forexample, text analysis module 3605 may be applied to the text data of acomment in order to generate a sentiment indicator 3801 associated witha comment. For example, as shown in FIG. 38, the sentiment indicator3801 generated by text analysis module 3605 may show that the commentsupports or opposes the policy (or neither). Similarly, the sentimentindicator 3801 generated by text analysis module 3605 may show that thecomment contains positive or negative language (or neither). Theinformation analyzed by text analysis module 3605 may also be used togenerate comment theme indicator 3803 and stance detection indicator3805.

FIG. 39 is a flow diagram of an example of a method 3900 for predictingwhether a regulation will be adopted. Method 3900 may, for example, beexecuted by one or more processors of a server (e.g., central server 105of FIG. 1) or any other appropriate hardware and/or software. Further,when executing method 3900, the one or more processors may executeinstructions stored in any one of the modules discussed above.

At step 3901, the server may access information scraped from theinternet to identify text data associated with comments expressed by aplurality of individuals about a proposed regulation. For example, thetext analytics system may access comments stored in database 3601,containing comments related to a specific proposed regulation.

At step 3903, the server may analyze the text data in order to determinea sentiment of each comment. For example, the server may analyze thetext data of a comment in order to determine a sentiment, which mayinclude matching at least one piece of text data from the comment to atleast one other piece of text data stored in a database. Furthermore,the text analytics filter may associate each comment with a predictedstance (e.g., where a stance may be supporting, opposing, or neutral),and the average stance may be taken as the predicted outcome of thepolicy, where if the average stance if negative, the predicted outcomeis for the policy to not be adopted in the current form, and if thecurrent stance is positive, the predicted outcome is for the policy tobe adopted.

At step 3905, the server may apply an influence filter to each commentto determine an influence metric associated with each comment. Asdescribed above, the influence metric may be determined using a varietyof factors. For example, dates of activity, the individual ororganizational author, whether the comment is a commonly-used templateor unique, and the like.

At step 3907, the server may weigh each comment using the influencemetric. For example, influence filter module 3607 may also determine thelevels of influence of each comment (e.g., based on the author of thecomment), and weigh each comment based on an influence level. Forexample, different commenters may be given a higher or lower influencelevel, which may then be applied to that author's comment. Other factorsthat may be considered may include who and where the comment is from,whether the author of the comment supports the policy, the gravitas ofthe commenter, what arguments are being made, and the like

At step 3909, the text analytics system determines, based on anaggregate of the weighted comments, an indicator associated withadoption of the regulation. For example, the indicator determined inthis step by indicator determination module 3609 is a likelihood measurereflecting a probability that the regulation will be adopted (e.g., apercentage).

At step 3911, the server may transmit the indicator to a system user.For example, this indicator may be a comparative indicator expressedthrough text (e.g., whether the policy is “more likely” or “less likely”to be approved when compared to another policy), expressed as apercentage (e.g., “53.5% chance for being promulgated unchanged in thisform,” “92.5% of Section 3 changing,”), or expressed in a number ofvotes (e.g., “43 Senators likely to vote yes”).

While the disclosure above has focused on primarily on predicting theoutcomes for comments made in relation to a regulation, and associatingcomments and stances to policies, and subsections of policies, theseanalyses may also be performed in other policy areas, such aslegislation. For example, comments made in support or opposition of apiece of legislation may be associated with specific sections of thelegislation, the stance and arguments of those comments may be computed,and a resulting prediction made to the likelihood of a section or theentirety of the legislation changing or being enacted.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, orother optical drive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules may be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules may bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. The steps of thedisclosed methods may be modified in any manner, including by reorderingsteps and/or inserting or deleting steps. Further, the section headingscontained herein are merely for ease of reference and are not limiting.It is intended, therefore, that the specification and examples beconsidered as illustrative only, with a true scope and spirit beingindicated by the following claims and their full scope of equivalents.

What is claimed is:
 1. A collaborative prediction system for alteringpredictive outcomes of dynamic processes, the system comprising: atleast one processor configured to: store initial information about apolicymaking process having a plurality of potentially differingoutcomes, wherein the initial information includes metadata describingthe policymaking process; train an influence model using machinelearning to calculate the influence of a policymaker associated with thepolicymaking process; train a first prediction model using machinelearning to assign to the policymaking process, based on a firstcalculation including an influence of a policymaker associated with thepolicymaking process assigned by the influence model, a first likelihoodof occurrence of at least one of the potentially differing outcomes;receive, from a first system user, notification data, the notificationdata including a user name and a customized tag associated with thepolicymaking process; transmit, based on the notification data, at leastsome of the stored initial information about the policymaking process toa second system user and a third system user; receive from the secondsystem user, first additional information responsive to the at leastsome of the stored initial information about the policymaking processand impacting the policymaking process, wherein the first additionalinformation includes a network of the policymaker associated with thepolicymaking process; train a second prediction model using machinelearning to generate, based on a second calculation including the firstadditional information and the initial information, a second likelihooddifferent from the first likelihood; transmit to the first system user,the second system user, and the third user an indication of the secondlikelihood; receive from the third system user, second additionalinformation impacting the policymaking process, wherein the secondadditional information represents a change to policymaking languageincluded in the policymaking process; train a third prediction modelusing machine learning to generate, based at least in part on a thirdcalculation including the second additional information and theinfluence of the policymaker associated with the policymaking process, athird likelihood different from the first likelihood and the secondlikelihood; and transmit to the first system user, the second systemuser, and the third system user an indication of the third likelihood,wherein the indication of the third likelihood is transmitted as part ofa graphical user interface including an electronic message for viewingon a web browser.
 2. The system of claim 1, wherein the notificationdata includes at least one of a message and an alert.
 3. The system ofclaim 1, wherein assigning a first likelihood includes accessing scrapeddata and generating the first likelihood based, at least in part, on theaccessed scraped data.
 4. The system of claim 3, wherein the accessedscraped data is based on a calculated similarity between partitioneddata sets.
 5. The system of claim 4, wherein the partitioned data setsinclude legislators in a legislative body associated with a legislativeproceeding.
 6. The system of claim 1, wherein the policymaking processis at least one of a legislative proceeding, a regulatory proceeding,and a court proceeding.
 7. The system of claim 1, wherein the at leastone processor is further configured to enable receipt of the first andsecond additional information, and the displaying to the first andsecond system users, via a communication-based dynamic workflowplatform.
 8. A collaborative prediction method for altering predictiveoutcomes of dynamic processes, the method comprising: storing initialinformation about a policymaking process having a plurality ofpotentially differing outcomes, wherein the initial information includesmetadata describing the policymaking process; training an influencemodel using machine learning to calculate the influence of a policymakerassociated with the policymaking process; training a first predictionmodel using machine learning to assign to the policymaking process,based on a first calculation including an influence of a policymakerassociated with the policymaking process assigned by the influencemodel, a first likelihood of occurrence of at least one of thepotentially differing outcomes; receiving, from a first system user,notification data, the notification data including a user name and acustomized tag associated with the policymaking process; transmitting,based on the notification data, at least some of the stored initialinformation about the policymaking process to a second system user and athird system user; receiving from the second system user, firstadditional information responsive to the at least some of the storedinitial information about the policymaking process and impacting thepolicymaking process, wherein the first additional information includesa network of the policymaker associated with the policymaking process;training a second prediction model using machine learning to generate,based on a second calculation including the first additional informationand the initial information, a second likelihood different from thefirst likelihood; transmitting to the first system user, the secondsystem user, and the third user an indication of the second likelihood;receiving from the third system user, second additional informationimpacting the policymaking process, wherein the second additionalinformation represents a change to policymaking language included in thepolicymaking process; training a third prediction model using machinelearning to generate, based at least in part on a third calculationincluding the second additional information and the influence of thepolicymaker associated with the policymaking process, a third likelihooddifferent from the first likelihood and the second likelihood; andtransmitting to the first system user, the second system user, and thethird system user an indication of the third likelihood, wherein theindication of the third likelihood is transmitted as part of a graphicaluser interface including an electronic message for viewing on a webbrowser.
 9. The method according to claim 8, wherein the notificationdata includes at least one of a message and an alert.
 10. The methodaccording to claim 8, wherein assigning a first likelihood includesaccessing scraped data and generating the first likelihood based, atleast in part, on the accessed scraped data.
 11. The method according toclaim 10, wherein the accessed scraped data is based on a calculatedsimilarity between partitioned data sets.
 12. The method according toclaim 11, wherein the partitioned data sets include legislators in alegislative body associated with a legislative proceeding.
 13. Themethod according to claim 8, wherein the policymaking process is atleast one of a legislative proceeding and a regulatory proceeding. 14.The method according to claim 10, further comprising: enabling receiptof the first and second additional information, and the displaying tothe first and second system users, via a communication-based dynamicworkflow platform.
 15. A non-transitory computer-readable mediumcomprising instructions that, when executed by at least one processor,cause the at least one processor to perform operations including:storing initial information about a policymaking process having aplurality of potentially differing outcomes, wherein the initialinformation includes metadata describing the policymaking process;training an influence model using machine learning to calculate theinfluence of a policymaker associated with the policymaking process;training a first prediction model using machine learning to assign tothe policymaking process, based on a first calculation including aninfluence of a policymaker associated with the policymaking processassigned by the influence model, a first likelihood of occurrence of atleast one of the potentially differing outcomes; receiving, from a firstsystem user, notification data, the notification data including a username and a customized tag associated with the policymaking process;transmitting, based on the notification data, at least some of thestored initial information about the policymaking process to a secondsystem user and a third system user; receiving from the second systemuser, first additional information responsive to the at least some ofthe stored initial information about the policymaking process andimpacting the policymaking process, wherein the first additionalinformation includes a network of the policymaker associated with thepolicymaking process; training a second prediction model using machinelearning to generate, based on a second calculation including the firstadditional information and the initial information, a second likelihooddifferent from the first likelihood; transmitting to the first systemuser, the second system user, and the third user an indication of thesecond likelihood; receiving from the third system user, secondadditional information impacting the policymaking process, wherein thesecond additional information represents a change to policymakinglanguage included in the policymaking process; training a thirdprediction model using machine learning to generate, based at least inpart on a third calculation including the second additional informationand the influence of the policymaker associated with the policymakingprocess, a third likelihood different from the first likelihood and thesecond likelihood; and transmitting to the first system user, the secondsystem user, and the third system user an indication of the thirdlikelihood, wherein the indication of the third likelihood istransmitted as part of a graphical user interface including anelectronic message for viewing on a web browser.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the notification dataincludes at least one of a message and an alert.
 17. The non-transitorycomputer-readable medium of claim 15, wherein assigning a firstlikelihood includes accessing at least one electronic data analogy andgenerating the first likelihood based, at least in part, on the accessedat least one electronic data analogy.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the accessed at least oneelectronic data analogy is based on a calculated similarity betweenpartitioned data sets.
 19. The non-transitory computer-readable mediumof claim 18, wherein the partitioned data sets include legislators in alegislative body associated with a legislative proceeding.
 20. Thenon-transitory computer-readable medium of claim 15, wherein thepolicymaking process is at least one of a legislative proceeding, aregulatory proceeding, and a court proceeding.
 21. The system of claim1, wherein the customized tag includes at least one of policy area,priority, issue type, engagement, and impact.
 22. The system of claim 1,wherein the influence model, the first prediction model, the secondprediction model, and the third prediction model each comprise at leastone of a logistic regression model, a support vector machine model, aNaive Bayes model, a neural network model, a decision tree model, or arandom forest model.
 23. The method according to claim 8, wherein theinfluence model, the first prediction model, the second predictionmodel, and the third prediction model each comprise at least one of alogistic regression model, a support vector machine model, a Naive Bayesmodel, a neural network model, a decision tree model, or a random forestmodel.
 24. The non-transitory computer-readable medium of claim 15,wherein the influence model, the first prediction model, the secondprediction model, and the third prediction model each comprise at leastone of a logistic regression model, a support vector machine model, aNaive Bayes model, a neural network model, a decision tree model, arandom forest model.